{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Kevin Class 1 and 2 Code with Notes.ipynb", "provenance": [], "authorship_tag": "ABX9TyOrBz12DoCW0BndWS7GsrK4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "rf5TiR6R3DG_", "colab_type": "code", "outputId": "8683d77e-017b-4bb1-ff08-56b723e71d47", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "import sklearn\n", "sklearn.__version__" ], "execution_count": 2, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'0.22.2.post1'" ] }, "metadata": { "tags": [] }, "execution_count": 2 } ] }, { "cell_type": "markdown", "metadata": { "id": "oX-Zkq6FAXg7", "colab_type": "text" }, "source": [ "# Part 1" ] }, { "cell_type": "code", "metadata": { "id": "vWb2JMBz3HNI", "colab_type": "code", "colab": {} }, "source": [ "import pandas as pd \n", "df = pd.read_csv('http://bit.ly/kaggletrain', nrows=10)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_txMKmwa3O6D", "colab_type": "code", "outputId": "0a3b1113-f872-46d0-a601-25e12b7f40d3", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df" ], "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

\n", "

	Parch	Fare
0	0	7.2500
1	0	71.2833
2	0	7.9250
3	0	53.1000
4	0	8.0500
5	0	8.4583
6	0	51.8625
7	1	21.0750
8	2	11.1333
9	0	30.0708

\n", "

" ], "text/plain": [ " Parch Fare\n", "0 0 7.2500\n", "1 0 71.2833\n", "2 0 7.9250\n", "3 0 53.1000\n", "4 0 8.0500\n", "5 0 8.4583\n", "6 0 51.8625\n", "7 1 21.0750\n", "8 2 11.1333\n", "9 0 30.0708" ] }, "metadata": { "tags": [] }, "execution_count": 6 } ] }, { "cell_type": "code", "metadata": { "id": "tNeriTgkBqHT", "colab_type": "code", "outputId": "15eb62b2-7d7f-428e-b178-a5d2efa0d6ae", "colab": { "base_uri": "https://localhost:8080/", "height": 223 } }, "source": [ "y = df['Survived']\n", "y" ], "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 0\n", "1 1\n", "2 1\n", "3 1\n", "4 0\n", "5 0\n", "6 0\n", "7 0\n", "8 1\n", "9 1\n", "Name: Survived, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 7 } ] }, { "cell_type": "code", "metadata": { "id": "_FbzeStFBu0N", "colab_type": "code", "outputId": "6c27e53c-c173-44c8-c40d-03de61049dde", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "y.shape" ], "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(10,)" ] }, "metadata": { "tags": [] }, "execution_count": 8 } ] }, { "cell_type": "code", "metadata": { "id": "l2Gs8h6lB_ig", "colab_type": "code", "colab": {} }, "source": [ "from sklearn.linear_model import LogisticRegression" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "kaAQmKB_CRoB", "colab_type": "code", "colab": {} }, "source": [ "logreg = LogisticRegression(solver='liblinear', random_state=1)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "GU3NvPWvDCsE", "colab_type": "code", "outputId": "4b49138b-00f1-4805-a1c8-64387cdc0858", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "from sklearn.model_selection import cross_val_score\n", "cross_val_score(logreg, X, y, cv=3, scoring='accuracy').mean()" ], "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.6944444444444443" ] }, "metadata": { "tags": [] }, "execution_count": 11 } ] }, { "cell_type": "code", "metadata": { "id": "m7Sm9kiMDP23", "colab_type": "code", "outputId": "a8b9c876-c7d6-4f77-8e47-ca2497b2511a", "colab": { "base_uri": "https://localhost:8080/", "height": 110 } }, "source": [ "logreg.fit(X, y)" ], "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", " multi_class='auto', n_jobs=None, penalty='l2',\n", " random_state=1, solver='liblinear', tol=0.0001, verbose=0,\n", " warm_start=False)" ] }, "metadata": { "tags": [] }, "execution_count": 12 } ] }, { "cell_type": "code", "metadata": { "id": "abHcsFXjD-KL", "colab_type": "code", "colab": {} }, "source": [ "df_new = pd.read_csv('http://bit.ly/kaggletest', nrows=10)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lcivpen2EPUX", "colab_type": "code", "outputId": "db2891b4-b920-457c-fc06-e7361e33779e", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df_new" ], "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	892	3	Kelly, Mr. James	male	34.5	0	0	330911	7.8292	NaN	Q
1	893	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7.0000	NaN	S
2	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
3	895	3	Wirz, Mr. Albert	male	27.0	0	0	315154	8.6625	NaN	S
4	896	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12.2875	NaN	S
5	897	3	Svensson, Mr. Johan Cervin	male	14.0	0	0	7538	9.2250	NaN	S
6	898	3	Connolly, Miss. Kate	female	30.0	0	0	330972	7.6292	NaN	Q
7	899	2	Caldwell, Mr. Albert Francis	male	26.0	1	1	248738	29.0000	NaN	S
8	900	3	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18.0	0	0	2657	7.2292	NaN	C
9	901	3	Davies, Mr. John Samuel	male	21.0	2	0	A/4 48871	24.1500	NaN	S

\n", "

" ], "text/plain": [ " PassengerId Pclass ... Cabin Embarked\n", "0 892 3 ... NaN Q\n", "1 893 3 ... NaN S\n", "2 894 2 ... NaN Q\n", "3 895 3 ... NaN S\n", "4 896 3 ... NaN S\n", "5 897 3 ... NaN S\n", "6 898 3 ... NaN Q\n", "7 899 2 ... NaN S\n", "8 900 3 ... NaN C\n", "9 901 3 ... NaN S\n", "\n", "[10 rows x 11 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 14 } ] }, { "cell_type": "code", "metadata": { "id": "jlSKQ8fFEQGx", "colab_type": "code", "outputId": "a2ae47a3-9656-48fa-9ab5-29f1e9003546", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "X_new = df_new[['Parch', 'Fare']]\n", "X_new" ], "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	Parch	Fare
0	0	7.8292
1	0	7.0000
2	0	9.6875
3	0	8.6625
4	1	12.2875
5	0	9.2250
6	0	7.6292
7	1	29.0000
8	0	7.2292
9	0	24.1500

\n", "

" ], "text/plain": [ " Parch Fare\n", "0 0 7.8292\n", "1 0 7.0000\n", "2 0 9.6875\n", "3 0 8.6625\n", "4 1 12.2875\n", "5 0 9.2250\n", "6 0 7.6292\n", "7 1 29.0000\n", "8 0 7.2292\n", "9 0 24.1500" ] }, "metadata": { "tags": [] }, "execution_count": 15 } ] }, { "cell_type": "markdown", "metadata": { "id": "63jU9XShFTSu", "colab_type": "text" }, "source": [ "# Part 2" ] }, { "cell_type": "code", "metadata": { "id": "DtInOd4kEU-m", "colab_type": "code", "outputId": "b9652515-ea71-4593-f619-fdd725a969e3", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df" ], "execution_count": 16, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

\n", "

" ], "text/plain": [ " PassengerId Survived Pclass ... Fare Cabin Embarked\n", "0 1 0 3 ... 7.2500 NaN S\n", "1 2 1 1 ... 71.2833 C85 C\n", "2 3 1 3 ... 7.9250 NaN S\n", "3 4 1 1 ... 53.1000 C123 S\n", "4 5 0 3 ... 8.0500 NaN S\n", "5 6 0 3 ... 8.4583 NaN Q\n", "6 7 0 1 ... 51.8625 E46 S\n", "7 8 0 3 ... 21.0750 NaN S\n", "8 9 1 3 ... 11.1333 NaN S\n", "9 10 1 2 ... 30.0708 NaN C\n", "\n", "[10 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 16 } ] }, { "cell_type": "code", "metadata": { "id": "Sh4IXLnfFcFO", "colab_type": "code", "outputId": "fe2b9dc6-f9fe-4354-bf28-89cf354603ba", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "from sklearn.preprocessing import OneHotEncoder \n", "ohe = OneHotEncoder(sparse=False) # Normally you dont see the sparse matrix. \\\n", " # set sparse = True in real life \n", "ohe.fit_transform(df[['Embarked']]) # Two brackets tell sklearn that this is a \n", " # FEATURE (column), not a row (a collection\n", " # of features). " ], "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[0., 0., 1.],\n", " [1., 0., 0.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [0., 1., 0.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [1., 0., 0.]])" ] }, "metadata": { "tags": [] }, "execution_count": 17 } ] }, { "cell_type": "code", "metadata": { "id": "0fIp-rbvITow", "colab_type": "code", "colab": {} }, "source": [ "# fit - learns from the data\n", "# transform - uses what it learned from fit \n", "# fit_transform is a transformer\n", "# it learns and then transforms \n", "# fit_transform - does both fitting and transform \n", "# if using both on a single dataset, use fit_transform instead of \n", "# fit and transform individually. " ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4K2l9dTLG3NI", "colab_type": "code", "outputId": "8670cb58-905c-4a10-f625-5962321a9555", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "ohe.categories_ # A trailing underscore_ means it was learned during the 'fit'\n", " # step" ], "execution_count": 19, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[array(['C', 'Q', 'S'], dtype=object)]" ] }, "metadata": { "tags": [] }, "execution_count": 19 } ] }, { "cell_type": "code", "metadata": { "id": "k4qSSHpZF4t1", "colab_type": "code", "outputId": "65ebcc1a-a3b6-481e-acb2-ef9dd8e89355", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df[['Embarked']]" ], "execution_count": 20, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	Embarked
0	S
1	C
2	S
3	S
4	S
5	Q
6	S
7	S
8	S
9	C

\n", "

" ], "text/plain": [ " Embarked\n", "0 S\n", "1 C\n", "2 S\n", "3 S\n", "4 S\n", "5 Q\n", "6 S\n", "7 S\n", "8 S\n", "9 C" ] }, "metadata": { "tags": [] }, "execution_count": 20 } ] }, { "cell_type": "code", "metadata": { "id": "vaPzXGbzGuR7", "colab_type": "code", "outputId": "6853570c-db30-4f50-bdbc-09d19f5e7419", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "ohe.fit_transform(df[['Embarked', 'Sex']])" ], "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[0., 0., 1., 0., 1.],\n", " [1., 0., 0., 1., 0.],\n", " [0., 0., 1., 1., 0.],\n", " [0., 0., 1., 1., 0.],\n", " [0., 0., 1., 0., 1.],\n", " [0., 1., 0., 0., 1.],\n", " [0., 0., 1., 0., 1.],\n", " [0., 0., 1., 0., 1.],\n", " [0., 0., 1., 1., 0.],\n", " [1., 0., 0., 1., 0.]])" ] }, "metadata": { "tags": [] }, "execution_count": 21 } ] }, { "cell_type": "code", "metadata": { "id": "wxsvdxCMJe0j", "colab_type": "code", "outputId": "a68e98fc-eaf8-4034-85c5-3210cd00fe33", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df[['Sex']]" ], "execution_count": 22, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	Sex
0	male
1	female
2	female
3	female
4	male
5	male
6	male
7	male
8	female
9	female

\n", "

" ], "text/plain": [ " Sex\n", "0 male\n", "1 female\n", "2 female\n", "3 female\n", "4 male\n", "5 male\n", "6 male\n", "7 male\n", "8 female\n", "9 female" ] }, "metadata": { "tags": [] }, "execution_count": 22 } ] }, { "cell_type": "markdown", "metadata": { "id": "pl5fru5VKI2r", "colab_type": "text" }, "source": [ "# Part 3" ] }, { "cell_type": "code", "metadata": { "id": "2MGr7s6EJist", "colab_type": "code", "colab": {} }, "source": [ "cols = ['Parch', 'Fare', 'Embarked', 'Sex']" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ddEOH0NKK6hg", "colab_type": "code", "outputId": "4a596984-4996-456b-da1f-893686ba7f05", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "X = df[cols]\n", "X" ], "execution_count": 24, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	Parch	Fare	Embarked	Sex
0	0	7.2500	S	male
1	0	71.2833	C	female
2	0	7.9250	S	female
3	0	53.1000	S	female
4	0	8.0500	S	male
5	0	8.4583	Q	male
6	0	51.8625	S	male
7	1	21.0750	S	male
8	2	11.1333	S	female
9	0	30.0708	C	female

\n", "

" ], "text/plain": [ " Parch Fare Embarked Sex\n", "0 0 7.2500 S male\n", "1 0 71.2833 C female\n", "2 0 7.9250 S female\n", "3 0 53.1000 S female\n", "4 0 8.0500 S male\n", "5 0 8.4583 Q male\n", "6 0 51.8625 S male\n", "7 1 21.0750 S male\n", "8 2 11.1333 S female\n", "9 0 30.0708 C female" ] }, "metadata": { "tags": [] }, "execution_count": 24 } ] }, { "cell_type": "code", "metadata": { "id": "DJa4DRtGK9Lo", "colab_type": "code", "colab": {} }, "source": [ "ohe = OneHotEncoder()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fWX4chDiLN8J", "colab_type": "code", "colab": {} }, "source": [ "from sklearn.compose import make_column_transformer" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "eT6RUz2ILTTI", "colab_type": "code", "colab": {} }, "source": [ "ct = make_column_transformer(\n", " (ohe, ['Embarked', 'Sex']), # ohe is applied to Embarked and Sex\n", " remainder='passthrough') # remainder cols are 'passedthrough'" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "wjwHR2PHL6wo", "colab_type": "code", "outputId": "abb1844f-24d2-42b0-a3ab-1cac1fc756ed", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "ct.fit_transform(X)" ], "execution_count": 28, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[ 0. , 0. , 1. , 0. , 1. , 0. , 7.25 ],\n", " [ 1. , 0. , 0. , 1. , 0. , 0. , 71.2833],\n", " [ 0. , 0. , 1. , 1. , 0. , 0. , 7.925 ],\n", " [ 0. , 0. , 1. , 1. , 0. , 0. , 53.1 ],\n", " [ 0. , 0. , 1. , 0. , 1. , 0. , 8.05 ],\n", " [ 0. , 1. , 0. , 0. , 1. , 0. , 8.4583],\n", " [ 0. , 0. , 1. , 0. , 1. , 0. , 51.8625],\n", " [ 0. , 0. , 1. , 0. , 1. , 1. , 21.075 ],\n", " [ 0. , 0. , 1. , 1. , 0. , 2. , 11.1333],\n", " [ 1. , 0. , 0. , 1. , 0. , 0. , 30.0708]])" ] }, "metadata": { "tags": [] }, "execution_count": 28 } ] }, { "cell_type": "code", "metadata": { "id": "S8Oxyx8bL8yg", "colab_type": "code", "colab": {} }, "source": [ "from sklearn.pipeline import make_pipeline \n", "pipe = make_pipeline(ct, logreg) # Two step pipeline " ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "bywNyWXTMoYj", "colab_type": "code", "outputId": "a840b600-5341-4f52-a5d1-da2571bcf0e0", "colab": { "base_uri": "https://localhost:8080/", "height": 428 } }, "source": [ "pipe.fit(X, y)\n", "# X 4 col goes to ct \n", "# ct turns it into 7 cols \n", "# this is sent to logreg \n", "# logreg then applies the logisticregressoin \n", "\n", "# pipe.fit modifies the pipe object in-place, so we dont have to \n", "# assign it to a variable\n", "\n", "# pipeline can end in a model or a transform. \n", "# for pipelines that end in a transform, we can use fit_transform()\n", "\n", "# You can cross validate a pipeline, \n", "# You DO NOT include cross validation inside a pipeline. " ], "execution_count": 30, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('columntransformer',\n", " ColumnTransformer(n_jobs=None, remainder='passthrough',\n", " sparse_threshold=0.3,\n", " transformer_weights=None,\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(categories='auto',\n", " drop=None,\n", " dtype=,\n", " handle_unknown='error',\n", " sparse=True),\n", " ['Embarked', 'Sex'])],\n", " verbose=False)),\n", " ('logisticregression',\n", " LogisticRegression(C=1.0, class_weight=None, dual=False,\n", " fit_intercept=True, intercept_scaling=1,\n", " l1_ratio=None, max_iter=100,\n", " multi_class='auto', n_jobs=None,\n", " penalty='l2', random_state=1,\n", " solver='liblinear', tol=0.0001, verbose=0,\n", " warm_start=False))],\n", " verbose=False)" ] }, "metadata": { "tags": [] }, "execution_count": 30 } ] }, { "cell_type": "code", "metadata": { "id": "PAYT-r3-Mzrk", "colab_type": "code", "outputId": "b4a81c7c-48f9-400e-b26a-662b1da98222", "colab": { "base_uri": "https://localhost:8080/", "height": 110 } }, "source": [ "logreg.fit(ct.fit_transform(X), y)" ], "execution_count": 31, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", " multi_class='auto', n_jobs=None, penalty='l2',\n", " random_state=1, solver='liblinear', tol=0.0001, verbose=0,\n", " warm_start=False)" ] }, "metadata": { "tags": [] }, "execution_count": 31 } ] }, { "cell_type": "code", "metadata": { "id": "RfIGSZ_WNsqz", "colab_type": "code", "outputId": "b410993a-955b-42e1-c702-265af9a6ede2", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "pipe.named_steps.logisticregression.coef_ # inspect a pipeline using named_steps\n", " # 7 coeffs because there were 7 features " ], "execution_count": 32, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[ 0.26491287, -0.19848033, -0.22907928, 1.0075062 , -1.17015293,\n", " 0.20056557, 0.01597307]])" ] }, "metadata": { "tags": [] }, "execution_count": 32 } ] }, { "cell_type": "code", "metadata": { "id": "AdXtqDZrN0yK", "colab_type": "code", "outputId": "cde25be7-e09a-4e81-fbf4-65cbe7cff380", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "X_new = df_new[cols]\n", "X_new" ], "execution_count": 33, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	Parch	Fare	Embarked	Sex
0	0	7.8292	Q	male
1	0	7.0000	S	female
2	0	9.6875	Q	male
3	0	8.6625	S	male
4	1	12.2875	S	female
5	0	9.2250	S	male
6	0	7.6292	Q	female
7	1	29.0000	S	male
8	0	7.2292	C	female
9	0	24.1500	S	male

\n", "

" ], "text/plain": [ " Parch Fare Embarked Sex\n", "0 0 7.8292 Q male\n", "1 0 7.0000 S female\n", "2 0 9.6875 Q male\n", "3 0 8.6625 S male\n", "4 1 12.2875 S female\n", "5 0 9.2250 S male\n", "6 0 7.6292 Q female\n", "7 1 29.0000 S male\n", "8 0 7.2292 C female\n", "9 0 24.1500 S male" ] }, "metadata": { "tags": [] }, "execution_count": 33 } ] }, { "cell_type": "code", "metadata": { "id": "ERSv-tJNPIem", "colab_type": "code", "outputId": "cb6467f4-cdd6-4d2a-8061-f3ef960d236e", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "pipe.predict(X_new) # did not run ct.fit_transform\n", " # only ct.transform()\n", " # apply the encodings it learned during fit \n", " # to apply on the test set" ], "execution_count": 34, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0])" ] }, "metadata": { "tags": [] }, "execution_count": 34 } ] }, { "cell_type": "code", "metadata": { "id": "MgPiuQnxPNqX", "colab_type": "code", "outputId": "a4e28216-956a-4de6-885f-52eb4933d531", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "logreg.predict(ct.transform(X_new)) # outputs 1 prediction for EVERY ROW" ], "execution_count": 35, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0])" ] }, "metadata": { "tags": [] }, "execution_count": 35 } ] }, { "cell_type": "markdown", "metadata": { "id": "eWeEC0WzQeU4", "colab_type": "text" }, "source": [ "# Recap \n", "\n", "http://bit.ly/basic-pipeline" ] }, { "cell_type": "code", "metadata": { "id": "3HuPXwyGPceA", "colab_type": "code", "colab": {} }, "source": [ "import pandas as pd\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.compose import make_column_transformer\n", "from sklearn.pipeline import make_pipeline" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "CdGS1a1CQz-S", "colab_type": "code", "colab": {} }, "source": [ "cols = ['Parch', 'Fare', 'Embarked', 'Sex']" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_NHjwkb9Q248", "colab_type": "code", "colab": {} }, "source": [ "df = pd.read_csv('http://bit.ly/kaggletrain', nrows=10)\n", "X = df[cols]\n", "y = df['Survived']" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "1Lbf-oVoQ5Sd", "colab_type": "code", "colab": {} }, "source": [ "df_new = pd.read_csv('http://bit.ly/kaggletest', nrows=10)\n", "X_new = df_new[cols]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "36r5nr5CQ7b7", "colab_type": "code", "colab": {} }, "source": [ "ohe = OneHotEncoder()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Nb7X0SbaQ-PL", "colab_type": "code", "colab": {} }, "source": [ "ct = make_column_transformer(\n", " (ohe, ['Embarked', 'Sex']),\n", " remainder='passthrough')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VextlfhuQ__N", "colab_type": "code", "colab": {} }, "source": [ "logreg = LogisticRegression(solver='liblinear', random_state=1)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "l3W8Jl3fRDgu", "colab_type": "code", "outputId": "b9b67262-755e-485c-fd1d-e452962507e3", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "pipe = make_pipeline(ct, logreg)\n", "pipe.fit(X, y)\n", "pipe.predict(X_new)" ], "execution_count": 43, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0])" ] }, "metadata": { "tags": [] }, "execution_count": 43 } ] }, { "cell_type": "markdown", "metadata": { "id": "1RHNNMRPUJiL", "colab_type": "text" }, "source": [ "# Part 4 \n" ] }, { "cell_type": "code", "metadata": { "id": "u74u5PfaRFhk", "colab_type": "code", "outputId": "b0df578c-2586-45d1-d800-361c171f2ee2", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df" ], "execution_count": 44, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

\n", "

" ], "text/plain": [ " PassengerId Survived Pclass ... Fare Cabin Embarked\n", "0 1 0 3 ... 7.2500 NaN S\n", "1 2 1 1 ... 71.2833 C85 C\n", "2 3 1 3 ... 7.9250 NaN S\n", "3 4 1 1 ... 53.1000 C123 S\n", "4 5 0 3 ... 8.0500 NaN S\n", "5 6 0 3 ... 8.4583 NaN Q\n", "6 7 0 1 ... 51.8625 E46 S\n", "7 8 0 3 ... 21.0750 NaN S\n", "8 9 1 3 ... 11.1333 NaN S\n", "9 10 1 2 ... 30.0708 NaN C\n", "\n", "[10 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 44 } ] }, { "cell_type": "code", "metadata": { "id": "CUyO2gxsUe3u", "colab_type": "code", "outputId": "1379c63b-106d-459d-8954-c7d7c08a904b", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "vect = CountVectorizer()\n", "dtm = vect.fit_transform(df['Name']) # CV wants a 1D object not a 2D hence \n", " # single brackets \n", "dtm # Document Term Matrix - Sparse Matrix" ], "execution_count": 45, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<10x40 sparse matrix of type ''\n", "\twith 46 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 45 } ] }, { "cell_type": "code", "metadata": { "id": "CqiSusI0VAXt", "colab_type": "code", "outputId": "24642b6c-b3fe-443c-ead4-38d8c255382f", "colab": { "base_uri": "https://localhost:8080/", "height": 56 } }, "source": [ "print(vect.get_feature_names()) # Think of them as col headings \n", " # Ignored words with less than a 2 letters " ], "execution_count": 46, "outputs": [ { "output_type": "stream", "text": [ "['achem', 'adele', 'allen', 'berg', 'bradley', 'braund', 'briggs', 'cumings', 'elisabeth', 'florence', 'futrelle', 'gosta', 'harris', 'heath', 'heikkinen', 'henry', 'jacques', 'james', 'john', 'johnson', 'laina', 'leonard', 'lily', 'master', 'may', 'mccarthy', 'miss', 'moran', 'mr', 'mrs', 'nasser', 'nicholas', 'oscar', 'owen', 'palsson', 'peel', 'thayer', 'timothy', 'vilhelmina', 'william']\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "k3ZnG20SVVyN", "colab_type": "code", "outputId": "75fd2f8e-821c-4044-cf80-69f59d9b4e2e", "colab": { "base_uri": "https://localhost:8080/", "height": 383 } }, "source": [ "pd.DataFrame(dtm.toarray(), columns=vect.get_feature_names()) # 10 x 40 matrix \n", " # CV counts how many \n", " # times a particular \n", " # 'feature' or word\n", " # appears \n", " # This is also a BOW\n", " # representation \n", " # of text\n", "# Docs are the rows and terms are the cols -> Document Term Matrix \n", "\n", "# With Text Data - DONT OVERTHINK IT. " ], "execution_count": 47, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	achem	adele	allen	berg	bradley	braund	briggs	cumings	elisabeth	florence	futrelle	gosta	harris	heath	heikkinen	henry	jacques	james	john	johnson	laina	leonard	lily	master	may	mccarthy	miss	moran	mr	mrs	nasser	nicholas	oscar	owen	palsson	peel	thayer	timothy	vilhelmina	william
0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
1	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0
2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
4	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0
7	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
8	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0
9	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0

\n", "

" ], "text/plain": [ " achem adele allen berg ... thayer timothy vilhelmina william\n", "0 0 0 0 0 ... 0 0 0 0\n", "1 0 0 0 0 ... 1 0 0 0\n", "2 0 0 0 0 ... 0 0 0 0\n", "3 0 0 0 0 ... 0 0 0 0\n", "4 0 0 1 0 ... 0 0 0 1\n", "5 0 0 0 0 ... 0 0 0 0\n", "6 0 0 0 0 ... 0 1 0 0\n", "7 0 0 0 0 ... 0 0 0 0\n", "8 0 0 0 1 ... 0 0 1 0\n", "9 1 1 0 0 ... 0 0 0 0\n", "\n", "[10 rows x 40 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 47 } ] }, { "cell_type": "code", "metadata": { "id": "XqQfNBkJV2yc", "colab_type": "code", "outputId": "62782c09-1af2-4a5d-dea1-d642e3d0ff51", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "cols = ['Parch', 'Fare', 'Embarked', 'Sex', 'Name']\n", "X = df[cols]\n", "X" ], "execution_count": 48, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	Parch	Fare	Embarked	Sex	Name
0	0	7.2500	S	male	Braund, Mr. Owen Harris
1	0	71.2833	C	female	Cumings, Mrs. John Bradley (Florence Briggs Th...
2	0	7.9250	S	female	Heikkinen, Miss. Laina
3	0	53.1000	S	female	Futrelle, Mrs. Jacques Heath (Lily May Peel)
4	0	8.0500	S	male	Allen, Mr. William Henry
5	0	8.4583	Q	male	Moran, Mr. James
6	0	51.8625	S	male	McCarthy, Mr. Timothy J
7	1	21.0750	S	male	Palsson, Master. Gosta Leonard
8	2	11.1333	S	female	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)
9	0	30.0708	C	female	Nasser, Mrs. Nicholas (Adele Achem)

\n", "

" ], "text/plain": [ " Parch Fare ... Sex Name\n", "0 0 7.2500 ... male Braund, Mr. Owen Harris\n", "1 0 71.2833 ... female Cumings, Mrs. John Bradley (Florence Briggs Th...\n", "2 0 7.9250 ... female Heikkinen, Miss. Laina\n", "3 0 53.1000 ... female Futrelle, Mrs. Jacques Heath (Lily May Peel)\n", "4 0 8.0500 ... male Allen, Mr. William Henry\n", "5 0 8.4583 ... male Moran, Mr. James\n", "6 0 51.8625 ... male McCarthy, Mr. Timothy J\n", "7 1 21.0750 ... male Palsson, Master. Gosta Leonard\n", "8 2 11.1333 ... female Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n", "9 0 30.0708 ... female Nasser, Mrs. Nicholas (Adele Achem)\n", "\n", "[10 rows x 5 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 48 } ] }, { "cell_type": "code", "metadata": { "id": "721YxtSdX4_w", "colab_type": "code", "colab": {} }, "source": [ "ct = make_column_transformer(\n", " (ohe, ['Embarked', 'Sex']),\n", " (vect, 'Name'), # CV expects 1D inputs, hence not ['Name']\n", " remainder='passthrough')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "pzBDstd6YNW3", "colab_type": "code", "outputId": "5c295dd3-172e-422e-eb63-3ecc47afa3bf", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "ct.fit_transform(X)" ], "execution_count": 50, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<10x47 sparse matrix of type ''\n", "\twith 78 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 50 } ] }, { "cell_type": "code", "metadata": { "id": "AVO7DmBCYROW", "colab_type": "code", "colab": {} }, "source": [ "pipe = make_pipeline(ct, logreg) # remaking the pipe object, hence the assignment\n", "pipe.fit(X, y);\n", "# pipe.fit(X, y); adding semicolon suprresses output in Jupyter Notebook" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ukf3rxynYmi2", "colab_type": "code", "outputId": "4e470077-646a-431a-c0d2-e1c9445f4bca", "colab": { "base_uri": "https://localhost:8080/", "height": 578 } }, "source": [ "pipe.named_steps" ], "execution_count": 52, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'columntransformer': ColumnTransformer(n_jobs=None, remainder='passthrough', sparse_threshold=0.3,\n", " transformer_weights=None,\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(categories='auto', drop=None,\n", " dtype=,\n", " handle_unknown='error',\n", " sparse=True),\n", " ['Embarked', 'Sex']),\n", " ('countvectorizer',\n", " CountVectorizer(analyzer='word', binary=False,\n", " decode_error='strict',\n", " dtype=,\n", " encoding='utf-8',\n", " input='content',\n", " lowercase=True, max_df=1.0,\n", " max_features=None, min_df=1,\n", " ngram_range=(1, 1),\n", " preprocessor=None,\n", " stop_words=None,\n", " strip_accents=None,\n", " token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", " tokenizer=None,\n", " vocabulary=None),\n", " 'Name')],\n", " verbose=False),\n", " 'logisticregression': LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", " multi_class='auto', n_jobs=None, penalty='l2',\n", " random_state=1, solver='liblinear', tol=0.0001, verbose=0,\n", " warm_start=False)}" ] }, "metadata": { "tags": [] }, "execution_count": 52 } ] }, { "cell_type": "code", "metadata": { "id": "FxUYj-rFY4qM", "colab_type": "code", "outputId": "c86f9459-d791-4b60-a548-4c4437e67ada", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "X_new = df_new[cols]\n", "pipe.predict(X_new)" ], "execution_count": 53, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0])" ] }, "metadata": { "tags": [] }, "execution_count": 53 } ] }, { "cell_type": "code", "metadata": { "id": "7sPs_OUNZBE8", "colab_type": "code", "colab": {} }, "source": [ "# Pipeline Type 1 \n", "# tranformer, tranformer, tranformer, tranformer, model\n", "# Pipeline Type 2 \n", "# tranformer, tranformer, tranformer, tranformer, tranformer" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Y4Uj0W8GCbOP", "colab_type": "text" }, "source": [ "# Part 5" ] }, { "cell_type": "code", "metadata": { "id": "pJldUcGRCcV6", "colab_type": "code", "outputId": "83b12d40-c00e-4d81-c35c-87035ba6101e", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "source": [ "df" ], "execution_count": 55, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

\n", "

	Parch	Fare	Embarked	Sex	Name	Age
0	0	7.2500	S	male	Braund, Mr. Owen Harris	22.0
1	0	71.2833	C	female	Cumings, Mrs. John Bradley (Florence Briggs Th...	38.0
2	0	7.9250	S	female	Heikkinen, Miss. Laina	26.0
3	0	53.1000	S	female	Futrelle, Mrs. Jacques Heath (Lily May Peel)	35.0
4	0	8.0500	S	male	Allen, Mr. William Henry	35.0
5	0	8.4583	Q	male	Moran, Mr. James	NaN
6	0	51.8625	S	male	McCarthy, Mr. Timothy J	54.0
7	1	21.0750	S	male	Palsson, Master. Gosta Leonard	2.0
8	2	11.1333	S	female	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	27.0
9	0	30.0708	C	female	Nasser, Mrs. Nicholas (Adele Achem)	14.0

\n", "

" ], "text/plain": [ " Parch Fare ... Name Age\n", "0 0 7.2500 ... Braund, Mr. Owen Harris 22.0\n", "1 0 71.2833 ... Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0\n", "2 0 7.9250 ... Heikkinen, Miss. Laina 26.0\n", "3 0 53.1000 ... Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0\n", "4 0 8.0500 ... Allen, Mr. William Henry 35.0\n", "5 0 8.4583 ... Moran, Mr. James NaN\n", "6 0 51.8625 ... McCarthy, Mr. Timothy J 54.0\n", "7 1 21.0750 ... Palsson, Master. Gosta Leonard 2.0\n", "8 2 11.1333 ... Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0\n", "9 0 30.0708 ... Nasser, Mrs. Nicholas (Adele Achem) 14.0\n", "\n", "[10 rows x 6 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 57 } ] }, { "cell_type": "code", "metadata": { "id": "MVfH26hsC_mI", "colab_type": "code", "colab": {} }, "source": [ "# pipe.fit(X, y)\n", "\n", "# Model can use and handle NaN and missing values.....which model? " ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "_K8U-ABHDEFY", "colab_type": "code", "colab": {} }, "source": [ "# option to handle missing values \n", "# 1. just drop - X.dropna()\n", "# good option under 2 condts \n", "# a. dropping very little data \n", "# b. NaNs are randomly distributed \n", "# 2. Drop the whole cols - X.dropna(axis='columns') - just DONT. \n", "# 3. Impute missing values - can be a course on its own! - You're MAKING up data" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "aE1EEaHKE3gv", "colab_type": "code", "outputId": "17051d19-9bb3-4744-cfa5-1e930d797cfe", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "from sklearn.impute import SimpleImputer \n", "\n", "imp = SimpleImputer()\n", "imp.fit_transform(df[['Age']]) # by default fills the missing cols with the \n", " # mean (median and mode are also an option)" ], "execution_count": 60, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[22. ],\n", " [38. ],\n", " [26. ],\n", " [35. ],\n", " [35. ],\n", " [28.11111111],\n", " [54. ],\n", " [ 2. ],\n", " [27. ],\n", " [14. ]])" ] }, "metadata": { "tags": [] }, "execution_count": 60 } ] }, { "cell_type": "code", "metadata": { "id": "2ypzf7QrE8k2", "colab_type": "code", "outputId": "2bf18617-6f2d-46ce-8f4a-8bd1cea589d3", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "imp.statistics_" ], "execution_count": 61, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([28.11111111])" ] }, "metadata": { "tags": [] }, "execution_count": 61 } ] }, { "cell_type": "code", "metadata": { "id": "XP82kVlmFhgD", "colab_type": "code", "outputId": "c2fb407b-8573-4ec4-d9c1-48a3462bb2da", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "ct = make_column_transformer(\n", " (ohe, ['Embarked', 'Sex']),\n", " (vect, 'Name'), # CV expects 1D inputs, hence not ['Name']\n", " (imp, ['Age']),\n", " remainder='passthrough')\n", "ct.fit_transform(X)" ], "execution_count": 62, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<10x48 sparse matrix of type ''\n", "\twith 88 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 62 } ] }, { "cell_type": "code", "metadata": { "id": "eVNS0timF7ST", "colab_type": "code", "outputId": "53a2b3b8-36f1-4df2-dbe6-40ed5492a204", "colab": { "base_uri": "https://localhost:8080/", "height": 671 } }, "source": [ "pipe = make_pipeline(ct, logreg)\n", "pipe.fit(X, y)\n", "pipe.named_steps" ], "execution_count": 63, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'columntransformer': ColumnTransformer(n_jobs=None, remainder='passthrough', sparse_threshold=0.3,\n", " transformer_weights=None,\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(categories='auto', drop=None,\n", " dtype=,\n", " handle_unknown='error',\n", " sparse=True),\n", " ['Embarked', 'Sex']),\n", " ('countvectorizer',\n", " CountVectorizer(analyzer='word', binary=False,\n", " decode_error='strict',\n", " dtype=...\n", " input='content',\n", " lowercase=True, max_df=1.0,\n", " max_features=None, min_df=1,\n", " ngram_range=(1, 1),\n", " preprocessor=None,\n", " stop_words=None,\n", " strip_accents=None,\n", " token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", " tokenizer=None,\n", " vocabulary=None),\n", " 'Name'),\n", " ('simpleimputer',\n", " SimpleImputer(add_indicator=False, copy=True,\n", " fill_value=None,\n", " missing_values=nan,\n", " strategy='mean', verbose=0),\n", " ['Age'])],\n", " verbose=False),\n", " 'logisticregression': LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", " multi_class='auto', n_jobs=None, penalty='l2',\n", " random_state=1, solver='liblinear', tol=0.0001, verbose=0,\n", " warm_start=False)}" ] }, "metadata": { "tags": [] }, "execution_count": 63 } ] }, { "cell_type": "code", "metadata": { "id": "YKhw7AGPGJjR", "colab_type": "code", "outputId": "c3570c0f-1470-48ae-a300-4131383c1773", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "X_new = df_new[cols]\n", "pipe.predict(X_new)\n", "# No imputation if X_new does not have any missing data in 'Age'\n", "# if X_new has missing values in 'Age' col, value that gets imputed is the \n", "# mean of 'age' in X and NOT the mean of 'Age' in X_new\n", "\n", "# You're only allowed to learn from training data \n", "# and then apply on training and testing\n", "# Imputer learns the values from the Training data, NOT THE TESTING DATA. " ], "execution_count": 64, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0])" ] }, "metadata": { "tags": [] }, "execution_count": 64 } ] }, { "cell_type": "code", "metadata": { "id": "YKz1mcn1GdLC", "colab_type": "code", "colab": {} }, "source": [ "# Predictions are made 1 row at a time - Conceptually \n", "# if a row in X_new has a missing value in Age, it is going to impute a\n", "# value that it learned from X" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "eotEq040IRiV", "colab_type": "code", "outputId": "d60a6364-745a-44ce-f2c9-a00c9afb3f8b", "colab": { "base_uri": "https://localhost:8080/", "height": 204 } }, "source": [ "# When imputing missing values - we can create a representation for all the \n", "# missing values \n", "imp_indicator = SimpleImputer(add_indicator=True)\n", "imp_indicator.fit_transform(X[['Age']])\n", "# Sometimes MISSINGNESS of a value can be a good indicator/feature in itself. " ], "execution_count": 66, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[22. , 0. ],\n", " [38. , 0. ],\n", " [26. , 0. ],\n", " [35. , 0. ],\n", " [35. , 0. ],\n", " [28.11111111, 1. ],\n", " [54. , 0. ],\n", " [ 2. , 0. ],\n", " [27. , 0. ],\n", " [14. , 0. ]])" ] }, "metadata": { "tags": [] }, "execution_count": 66 } ] }, { "cell_type": "code", "metadata": { "id": "Y1I4vxu6I4Uu", "colab_type": "code", "colab": {} }, "source": [ "# Some other types of imputers that might give better results \n", "# Iterative Imputer " ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "pjEIdkjLKuh0", "colab_type": "text" }, "source": [ "# Part 6 " ] }, { "cell_type": "code", "metadata": { "id": "BVGs1sLwKvUv", "colab_type": "code", "outputId": "428f6e84-c0d4-454c-e196-00e64db38e7d", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "df = pd.read_csv('http://bit.ly/kaggletrain')\n", "df.shape" ], "execution_count": 68, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(891, 12)" ] }, "metadata": { "tags": [] }, "execution_count": 68 } ] }, { "cell_type": "code", "metadata": { "id": "2JmESlRILIkR", "colab_type": "code", "outputId": "7530c830-3493-4922-dc69-dbf1714984bf", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "df_new = pd.read_csv('http://bit.ly/kaggletest')\n", "df_new.shape" ], "execution_count": 69, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(418, 11)" ] }, "metadata": { "tags": [] }, "execution_count": 69 } ] }, { "cell_type": "code", "metadata": { "id": "pFAYl1WlLOHZ", "colab_type": "code", "outputId": "48c3681c-9281-4a42-a50c-7250ef7baf33", "colab": { "base_uri": "https://localhost:8080/", "height": 260 } }, "source": [ "df.isna().sum()" ], "execution_count": 70, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 70 } ] }, { "cell_type": "code", "metadata": { "id": "sRmBDi0lLXQS", "colab_type": "code", "outputId": "2150af02-daba-43dc-8201-0b59b13ca7b1", "colab": { "base_uri": "https://localhost:8080/", "height": 241 } }, "source": [ "df_new.isna().sum()" ], "execution_count": 71, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "PassengerId 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 86\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 1\n", "Cabin 327\n", "Embarked 0\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 71 } ] }, { "cell_type": "code", "metadata": { "id": "cO3W5hZcLdiL", "colab_type": "code", "colab": {} }, "source": [ "# Embarked missing in training set \n", "# Fare missing vals in Testing Set " ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "tAh6UyVdL00Q", "colab_type": "code", "colab": {} }, "source": [ "X = df[cols]\n", "y = df['Survived']" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AVeQbiIvMB-3", "colab_type": "code", "outputId": "95bfe14d-230b-433c-e5d2-ec97c558c534", "colab": { "base_uri": "https://localhost:8080/", "height": 367 } }, "source": [ "ct = make_column_transformer(\n", " (ohe, ['Embarked', 'Sex']),\n", " (vect, 'Name'), # CV expects 1D inputs, hence not ['Name']\n", " (imp, ['Age']),\n", " remainder='passthrough')\n", "ct.fit_transform(X) # embarked has missing values, hence the error! " ], "execution_count": 74, "outputs": [ { "output_type": "error", "ename": "ValueError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m remainder='passthrough')\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# embarked has missing values, hence the error!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 516\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_remainder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 518\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_fit_transform_one\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/compose/_column_transformer.py\u001b[0m in \u001b[0;36m_fit_transform\u001b[0;34m(self, X, y, func, fitted)\u001b[0m\n\u001b[1;32m 455\u001b[0m message=self._log_message(name, idx, len(transformers)))\n\u001b[1;32m 456\u001b[0m for idx, (name, trans, column, weight) in enumerate(\n\u001b[0;32m--> 457\u001b[0;31m self._iter(fitted=fitted, replace_strings=True), 1))\n\u001b[0m\u001b[1;32m 458\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"Expected 2D array, got 1D array instead\"\u001b[0m \u001b[0;32min\u001b[0m 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imputer is going to FILL IN 'missing' for 'NaN'. It will become a \n", "# 4th column during OHE. \n", "# Recommended by 'Researchers'." ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "gZQAp9_6Nf30", "colab_type": "code", "outputId": "bafb26c6-829f-4078-f78c-dc60897cafd4", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "imp_ohe = make_pipeline(imp_constant, ohe) # TRANSFORMER ONLY PIPELINE - Optimus Prime Loves it\n", "imp_ohe.fit_transform(X[['Embarked']])" ], "execution_count": 76, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<891x4 sparse matrix of type ''\n", "\twith 891 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 76 } ] }, { "cell_type": "code", "metadata": { "id": "J61bY3w_Nxhd", "colab_type": "code", "outputId": "a4652bae-bae0-42bc-a3e3-ef5db8be206e", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "ohe.fit_transform(imp_constant.fit_transform(X[['Embarked']]))" ], "execution_count": 77, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<891x4 sparse matrix of type ''\n", "\twith 891 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 77 } ] }, { "cell_type": "code", "metadata": { "id": "8EbXNo00N8h7", "colab_type": "code", "colab": {} }, "source": [ "# All pipeline steps OTHER THAN THE FINAL STEP must be a transformer. \n", "# The Final Step CAN be a Transformer or a Model - The Two types of pipelines\n", "\n", "# Embarked missing in training set \n", "ct = make_column_transformer(\n", " (imp_ohe, ['Embarked', 'Sex']), # Pipeline that ACTS as a Transformer\n", " (vect, 'Name'), # CV expects 1D inputs, hence not ['Name']\n", " (imp, ['Age']),\n", " remainder='passthrough')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "fn1YD5o4O5Vj", "colab_type": "code", "outputId": "6e682210-ff62-4eb0-f7ff-c656f7dce91b", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "ct.fit_transform(X)" ], "execution_count": 79, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<891x1518 sparse matrix of type ''\n", "\twith 7328 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 79 } ] }, { "cell_type": "code", "metadata": { "id": "jRH2DLwMPBEE", "colab_type": "code", "outputId": "aca0b072-851d-486d-828c-4e2e561e9f7c", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "# Fare missing vals in Testing Set \n", "ct = make_column_transformer(\n", " (imp_ohe, ['Embarked', 'Sex']), # Pipeline that ACTS as a Transformer\n", " (vect, 'Name'), # CV expects 1D inputs, hence not ['Name']\n", " (imp, ['Age', 'Fare']), # Learn the mean value of Fare during train, apply on test later\n", " remainder='passthrough')\n", "ct.fit_transform(X)" ], "execution_count": 80, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<891x1518 sparse matrix of type ''\n", "\twith 7328 stored elements in Compressed Sparse Row format>" ] }, "metadata": { "tags": [] }, "execution_count": 80 } ] }, { "cell_type": "code", "metadata": { "id": "7eXvS1kCPqTw", "colab_type": "code", "colab": {} }, "source": [ "pipe = make_pipeline(ct, logreg)\n", "pipe.fit(X, y);" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "J9V5DurAP1Q2", "colab_type": "code", "outputId": "4eaf5cfc-733e-431e-8280-57a136d82f49", "colab": { "base_uri": "https://localhost:8080/", "height": 372 } }, "source": [ "X_new = df_new[cols]\n", "pipe.predict(X_new)" ], "execution_count": 82, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n", " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n", " 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n", " 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n", " 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n", " 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n", " 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n", " 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n", " 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n", " 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n", " 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n", " 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n", " 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1])" ] }, "metadata": { "tags": [] }, "execution_count": 82 } ] }, { "cell_type": "code", "metadata": { "id": "vzGaZYKNP6Dv", "colab_type": "code", "outputId": "77ff4bf6-7e1b-49b4-faaf-cb8f1d49783d", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "ct.named_transformers_.simpleimputer.statistics_" ], "execution_count": 83, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([29.69911765, 32.20420797])" ] }, "metadata": { "tags": [] }, "execution_count": 83 } ] }, { "cell_type": "code", "metadata": { "id": "kY5xidfmRAHj", "colab_type": "code", "outputId": "c8908d7b-fedd-4372-9909-1464f8015b44", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "ct.named_transformers_.pipeline.named_steps.simpleimputer.statistics_" ], "execution_count": 84, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['missing', 'missing'], dtype=object)" ] }, "metadata": { "tags": [] }, "execution_count": 84 } ] }, { "cell_type": "markdown", "metadata": { "id": "NDBGLGE7RgAw", "colab_type": "text" }, "source": [ "# Recap \n", "\n", "http://bit.ly/complex-pipeline" ] }, { "cell_type": "code", "metadata": { "id": "6YgTDO7pRHKu", "colab_type": "code", "colab": {} }, "source": [ "import pandas as pd\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.compose import make_column_transformer\n", "from sklearn.pipeline import make_pipeline" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "4xGpIlH2RrIW", "colab_type": "code", "colab": {} }, "source": [ "cols = ['Parch', 'Fare', 'Embarked', 'Sex', 'Name', 'Age']" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AqTdkCKpRs9c", "colab_type": "code", "colab": {} }, "source": [ "df = pd.read_csv('http://bit.ly/kaggletrain')\n", "X = df[cols]\n", "y = df['Survived']" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "j5xGA9zIRueq", "colab_type": "code", "colab": {} }, "source": [ "df_new = pd.read_csv('http://bit.ly/kaggletest')\n", "X_new = df_new[cols]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "R1-0KlSlRxL-", "colab_type": "code", "colab": {} }, "source": [ "imp_constant = SimpleImputer(strategy='constant', fill_value='missing')\n", "ohe = OneHotEncoder()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Sy-cCxkWRy5v", "colab_type": "code", "colab": {} }, "source": [ "imp_ohe = make_pipeline(imp_constant, ohe)\n", "vect = CountVectorizer()\n", "imp = SimpleImputer()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KdX_oZOZR0dt", "colab_type": "code", "colab": {} }, "source": [ "ct = make_column_transformer(\n", " (imp_ohe, ['Embarked', 'Sex']),\n", " (vect, 'Name'),\n", " (imp, ['Age', 'Fare']),\n", " remainder='passthrough')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "wj32_ioMR2Fc", "colab_type": "code", "colab": {} }, "source": [ "logreg = LogisticRegression(solver='liblinear', random_state=1)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "p_lFT-QwR4lX", "colab_type": "code", "outputId": "6a0a4bd3-569f-49a4-be44-0acb724b6baf", "colab": { "base_uri": "https://localhost:8080/", "height": 372 } }, "source": [ "pipe = make_pipeline(ct, logreg)\n", "pipe.fit(X, y)\n", "pipe.predict(X_new)" ], "execution_count": 93, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,\n", " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n", " 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n", " 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n", " 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n", " 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n", " 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n", " 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n", " 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n", " 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n", " 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n", " 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n", " 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1])" ] }, "metadata": { "tags": [] }, "execution_count": 93 } ] }, { "cell_type": "markdown", "metadata": { "id": "7R9rZSDLYnuh", "colab_type": "text" }, "source": [ "# Part 7 \n", "Model Evaluation" ] }, { "cell_type": "code", "metadata": { "id": "1ZTVTGFWR6u3", "colab_type": "code", "outputId": "15f29b10-3116-4fbe-952b-7b6fa1905a51", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "from sklearn.model_selection import cross_val_score\n", "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()\n", "# does the data transformations after splitting, in order to avoid data-leakage " ], "execution_count": 94, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8114619295712762" ] }, "metadata": { "tags": [] }, "execution_count": 94 } ] }, { "cell_type": "code", "metadata": { "id": "7X9SpSkLZB22", "colab_type": "code", "outputId": "defd754e-fb30-48cd-ce33-32c7d1d7cb77", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "pipe.named_steps.keys()" ], "execution_count": 95, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['columntransformer', 'logisticregression'])" ] }, "metadata": { "tags": [] }, "execution_count": 95 } ] }, { "cell_type": "code", "metadata": { "id": "fYJBzoDnaQ5F", "colab_type": "code", "outputId": "b5d90d72-1d0c-413c-aee4-c979e3d3d809", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "params = {}\n", "params['logisticregression__penalty'] = ['l1', 'l2']\n", "params['logisticregression__C'] = [0.1, 1, 10]\n", "params" ], "execution_count": 96, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'logisticregression__C': [0.1, 1, 10],\n", " 'logisticregression__penalty': ['l1', 'l2']}" ] }, "metadata": { "tags": [] }, "execution_count": 96 } ] }, { "cell_type": "code", "metadata": { "id": "k7mQh_rgao4t", "colab_type": "code", "colab": {} }, "source": [ "from sklearn.model_selection import GridSearchCV\n", "\n", "grid = GridSearchCV(pipe, params, cv=5, scoring='accuracy')\n", "grid.fit(X, y);" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0_n7qde6bOX6", "colab_type": "code", "outputId": "b47d3c3c-359a-4b2b-98c2-e997470e49fa", "colab": { "base_uri": "https://localhost:8080/", "height": 362 } }, "source": [ "results = pd.DataFrame(grid.cv_results_)\n", "results " ], "execution_count": 98, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_logisticregression__C	param_logisticregression__penalty	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
0	0.020536	0.001994	0.008072	0.000920	0.1	l1	{'logisticregression__C': 0.1, 'logisticregres...	0.787709	0.803371	0.769663	0.758427	0.797753	0.783385	0.016946	6
1	0.019984	0.001300	0.007914	0.000710	0.1	l2	{'logisticregression__C': 0.1, 'logisticregres...	0.798883	0.803371	0.764045	0.775281	0.803371	0.788990	0.016258	5
2	0.022554	0.001796	0.008289	0.000884	1	l1	{'logisticregression__C': 1, 'logisticregressi...	0.815642	0.820225	0.797753	0.792135	0.848315	0.814814	0.019787	2
3	0.021585	0.000745	0.007627	0.000161	1	l2	{'logisticregression__C': 1, 'logisticregressi...	0.798883	0.825843	0.803371	0.786517	0.842697	0.811462	0.020141	3
4	0.029178	0.001424	0.007903	0.000209	10	l1	{'logisticregression__C': 10, 'logisticregress...	0.832402	0.797753	0.825843	0.775281	0.853933	0.817042	0.027533	1
5	0.024294	0.004325	0.007610	0.000157	10	l2	{'logisticregression__C': 10, 'logisticregress...	0.782123	0.803371	0.808989	0.797753	0.853933	0.809234	0.024080	4

\n", "

" ], "text/plain": [ " mean_fit_time std_fit_time ... std_test_score rank_test_score\n", "0 0.020536 0.001994 ... 0.016946 6\n", "1 0.019984 0.001300 ... 0.016258 5\n", "2 0.022554 0.001796 ... 0.019787 2\n", "3 0.021585 0.000745 ... 0.020141 3\n", "4 0.029178 0.001424 ... 0.027533 1\n", "5 0.024294 0.004325 ... 0.024080 4\n", "\n", "[6 rows x 15 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 98 } ] }, { "cell_type": "code", "metadata": { "id": "wGoJOPGCbTLT", "colab_type": "code", "outputId": "b5488b2b-5cfd-4b6b-ddea-fa510213357b", "colab": { "base_uri": "https://localhost:8080/", "height": 362 } }, "source": [ "results.sort_values('rank_test_score')" ], "execution_count": 99, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_logisticregression__C	param_logisticregression__penalty	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
4	0.029178	0.001424	0.007903	0.000209	10	l1	{'logisticregression__C': 10, 'logisticregress...	0.832402	0.797753	0.825843	0.775281	0.853933	0.817042	0.027533	1
2	0.022554	0.001796	0.008289	0.000884	1	l1	{'logisticregression__C': 1, 'logisticregressi...	0.815642	0.820225	0.797753	0.792135	0.848315	0.814814	0.019787	2
3	0.021585	0.000745	0.007627	0.000161	1	l2	{'logisticregression__C': 1, 'logisticregressi...	0.798883	0.825843	0.803371	0.786517	0.842697	0.811462	0.020141	3
5	0.024294	0.004325	0.007610	0.000157	10	l2	{'logisticregression__C': 10, 'logisticregress...	0.782123	0.803371	0.808989	0.797753	0.853933	0.809234	0.024080	4
1	0.019984	0.001300	0.007914	0.000710	0.1	l2	{'logisticregression__C': 0.1, 'logisticregres...	0.798883	0.803371	0.764045	0.775281	0.803371	0.788990	0.016258	5
0	0.020536	0.001994	0.008072	0.000920	0.1	l1	{'logisticregression__C': 0.1, 'logisticregres...	0.787709	0.803371	0.769663	0.758427	0.797753	0.783385	0.016946	6

\n", "

" ], "text/plain": [ " mean_fit_time std_fit_time ... std_test_score rank_test_score\n", "4 0.029178 0.001424 ... 0.027533 1\n", "2 0.022554 0.001796 ... 0.019787 2\n", "3 0.021585 0.000745 ... 0.020141 3\n", "5 0.024294 0.004325 ... 0.024080 4\n", "1 0.019984 0.001300 ... 0.016258 5\n", "0 0.020536 0.001994 ... 0.016946 6\n", "\n", "[6 rows x 15 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 99 } ] }, { "cell_type": "code", "metadata": { "id": "fFFUi8kfbepS", "colab_type": "code", "outputId": "0693337c-3029-4d79-cd16-c751a2c6c346", "colab": { "base_uri": "https://localhost:8080/", "height": 372 } }, "source": [ "pipe.named_steps.columntransformer.named_transformers_" ], "execution_count": 100, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'countvectorizer': CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n", " dtype=, encoding='utf-8', input='content',\n", " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", " ngram_range=(1, 1), preprocessor=None, stop_words=None,\n", " strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", " tokenizer=None, vocabulary=None),\n", " 'pipeline': Pipeline(memory=None,\n", " steps=[('simpleimputer',\n", " SimpleImputer(add_indicator=False, copy=True,\n", " fill_value='missing', missing_values=nan,\n", " strategy='constant', verbose=0)),\n", " ('onehotencoder',\n", " OneHotEncoder(categories='auto', drop=None,\n", " dtype=,\n", " handle_unknown='error', sparse=True))],\n", " verbose=False),\n", " 'remainder': 'passthrough',\n", " 'simpleimputer': SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n", " missing_values=nan, strategy='mean', verbose=0)}" ] }, "metadata": { "tags": [] }, "execution_count": 100 } ] }, { "cell_type": "code", "metadata": { "id": "Ud9-bdBZc6Zg", "colab_type": "code", "outputId": "ceb5477b-7a4a-491c-ab58-577648991195", "colab": { "base_uri": "https://localhost:8080/", "height": 110 } }, "source": [ "params['columntransformer__pipeline__onehotencoder__drop'] = [None, 'first']\n", "params['columntransformer__countvectorizer__ngram_range'] = [(1,1), (1,2)]\n", "params['columntransformer__simpleimputer__add_indicator'] = [False, True]\n", "params \n", "# We can name the pipelines to make this shorter, but in nested pipelines,\n", "# its going to be chaos anyway " ], "execution_count": 101, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'columntransformer__countvectorizer__ngram_range': [(1, 1), (1, 2)],\n", " 'columntransformer__pipeline__onehotencoder__drop': [None, 'first'],\n", " 'columntransformer__simpleimputer__add_indicator': [False, True],\n", " 'logisticregression__C': [0.1, 1, 10],\n", " 'logisticregression__penalty': ['l1', 'l2']}" ] }, "metadata": { "tags": [] }, "execution_count": 101 } ] }, { "cell_type": "code", "metadata": { "id": "bgjzDSxeeMZc", "colab_type": "code", "colab": {} }, "source": [ "grid = GridSearchCV(pipe, params, cv=5, scoring='accuracy')\n", "grid.fit(X, y);" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "7DjSiMssZn2s", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "f7be73aa-ebbf-417d-e12d-b8c80a15ac25" }, "source": [ "results = pd.DataFrame(grid.cv_results_)\n", "results.sort_values('rank_test_score')" ], "execution_count": 103, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "

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	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_columntransformer__countvectorizer__ngram_range	param_columntransformer__pipeline__onehotencoder__drop	param_columntransformer__simpleimputer__add_indicator	param_logisticregression__C	param_logisticregression__penalty	params	split0_test_score	split1_test_score	split2_test_score	split3_test_score	split4_test_score	mean_test_score	std_test_score	rank_test_score
28	0.041511	0.003645	0.009240	0.001150	(1, 2)	None	False	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.849162	0.820225	0.820225	0.786517	0.859551	0.827136	0.025618	1
46	0.052019	0.006278	0.009041	0.000225	(1, 2)	first	True	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.854749	0.825843	0.820225	0.775281	0.859551	0.827129	0.030171	2
34	0.041211	0.004972	0.008777	0.000430	(1, 2)	None	True	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.843575	0.820225	0.820225	0.786517	0.859551	0.826018	0.024739	3
40	0.051974	0.004799	0.009305	0.000456	(1, 2)	first	False	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.837989	0.820225	0.808989	0.780899	0.853933	0.820407	0.024998	4
22	0.037861	0.004578	0.008212	0.000281	(1, 1)	first	True	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.815642	0.808989	0.825843	0.786517	0.859551	0.819308	0.023905	5
10	0.030084	0.002975	0.008430	0.000624	(1, 1)	None	True	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.821229	0.814607	0.814607	0.786517	0.853933	0.818178	0.021520	6
16	0.037340	0.004994	0.008273	0.000329	(1, 1)	first	False	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.821229	0.803371	0.825843	0.780899	0.853933	0.817055	0.024286	7
4	0.030183	0.001353	0.007829	0.000198	(1, 1)	None	False	10	l1	{'columntransformer__countvectorizer__ngram_ra...	0.832402	0.797753	0.825843	0.775281	0.853933	0.817042	0.027533	8
20	0.023119	0.001172	0.007894	0.000250	(1, 1)	first	True	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.810056	0.820225	0.797753	0.792135	0.853933	0.814820	0.021852	9
44	0.029346	0.001732	0.008617	0.000558	(1, 2)	first	True	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.810056	0.820225	0.797753	0.792135	0.853933	0.814820	0.021852	9
2	0.021188	0.000679	0.008124	0.001334	(1, 1)	None	False	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.815642	0.820225	0.797753	0.792135	0.848315	0.814814	0.019787	11
47	0.028825	0.000462	0.009271	0.001721	(1, 2)	first	True	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.820225	0.820225	0.780899	0.853933	0.812598	0.026265	12
8	0.022105	0.000583	0.007783	0.000269	(1, 1)	None	True	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.804469	0.820225	0.786517	0.792135	0.859551	0.812579	0.026183	13
38	0.028299	0.000755	0.009125	0.001697	(1, 2)	first	False	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.804469	0.820225	0.797753	0.792135	0.848315	0.812579	0.020194	14
14	0.021598	0.000780	0.007579	0.000300	(1, 1)	first	False	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.804469	0.820225	0.797753	0.792135	0.848315	0.812579	0.020194	14
11	0.023982	0.002240	0.007904	0.000187	(1, 1)	None	True	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.782123	0.803371	0.808989	0.792135	0.870787	0.811481	0.031065	16
21	0.020800	0.000512	0.007781	0.000222	(1, 1)	first	True	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.793296	0.820225	0.803371	0.786517	0.853933	0.811468	0.024076	17
3	0.020964	0.000622	0.007585	0.000215	(1, 1)	None	False	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.825843	0.803371	0.786517	0.842697	0.811462	0.020141	18
26	0.028059	0.000550	0.008518	0.000375	(1, 2)	None	False	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.810056	0.820225	0.786517	0.792135	0.848315	0.811449	0.022058	19
23	0.023505	0.002672	0.008047	0.000055	(1, 1)	first	True	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.776536	0.803371	0.808989	0.792135	0.870787	0.810363	0.032182	20
9	0.020925	0.000431	0.007636	0.000115	(1, 1)	None	True	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.793296	0.825843	0.797753	0.786517	0.848315	0.810345	0.023233	21
15	0.020606	0.000601	0.007823	0.000259	(1, 1)	first	False	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.804469	0.820225	0.803371	0.786517	0.837079	0.810332	0.017107	22
17	0.022542	0.000978	0.007953	0.000845	(1, 1)	first	False	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.782123	0.803371	0.808989	0.797753	0.853933	0.809234	0.024080	23
35	0.028479	0.000384	0.008383	0.000058	(1, 2)	None	True	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.782123	0.820225	0.814607	0.780899	0.848315	0.809234	0.025357	23
5	0.024417	0.001988	0.008097	0.000521	(1, 1)	None	False	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.782123	0.803371	0.808989	0.797753	0.853933	0.809234	0.024080	23
32	0.029279	0.001644	0.008754	0.000282	(1, 2)	None	True	1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.820225	0.780899	0.792135	0.853933	0.809215	0.025773	26
29	0.028205	0.000189	0.008363	0.000408	(1, 2)	None	False	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.814607	0.820225	0.780899	0.837079	0.808104	0.020904	27
45	0.027212	0.000554	0.008392	0.000167	(1, 2)	first	True	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.793296	0.814607	0.797753	0.786517	0.848315	0.808097	0.022143	28
41	0.028515	0.001236	0.008203	0.000117	(1, 2)	first	False	10	l2	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.814607	0.820225	0.780899	0.831461	0.806980	0.019414	29
39	0.027183	0.000146	0.008443	0.000363	(1, 2)	first	False	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.808989	0.797753	0.786517	0.837079	0.805844	0.017164	30
27	0.027390	0.000769	0.008642	0.000421	(1, 2)	None	False	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.814607	0.792135	0.786517	0.837079	0.805844	0.018234	30
33	0.027522	0.000263	0.009554	0.001630	(1, 2)	None	True	1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.782123	0.814607	0.792135	0.786517	0.848315	0.804739	0.024489	32
31	0.025574	0.000371	0.008244	0.000092	(1, 2)	None	True	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.803371	0.769663	0.786517	0.814607	0.794608	0.015380	33
7	0.020743	0.001260	0.007716	0.000091	(1, 1)	None	True	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.803371	0.764045	0.786517	0.814607	0.793484	0.017253	34
19	0.019558	0.000189	0.007791	0.000055	(1, 1)	first	True	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.793296	0.803371	0.764045	0.780899	0.814607	0.791243	0.017572	35
43	0.026156	0.001185	0.008566	0.000397	(1, 2)	first	True	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.797753	0.764045	0.780899	0.808989	0.790114	0.015849	36
37	0.024954	0.000290	0.008205	0.000161	(1, 2)	first	False	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.803371	0.764045	0.780899	0.808989	0.789003	0.016100	37
25	0.025537	0.000881	0.008096	0.000086	(1, 2)	None	False	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.793296	0.803371	0.764045	0.775281	0.808989	0.788996	0.016944	38
1	0.019058	0.000234	0.007786	0.000465	(1, 1)	None	False	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.798883	0.803371	0.764045	0.775281	0.803371	0.788990	0.016258	39
13	0.018940	0.000372	0.007500	0.000182	(1, 1)	first	False	0.1	l2	{'columntransformer__countvectorizer__ngram_ra...	0.782123	0.803371	0.764045	0.780899	0.808989	0.787885	0.016343	40
0	0.019134	0.000593	0.007628	0.000628	(1, 1)	None	False	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.803371	0.769663	0.758427	0.797753	0.783385	0.016946	41
30	0.024928	0.000464	0.008405	0.000205	(1, 2)	None	True	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.803371	0.769663	0.758427	0.797753	0.783385	0.016946	41
24	0.026180	0.001312	0.008715	0.000487	(1, 2)	None	False	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.803371	0.769663	0.758427	0.797753	0.783385	0.016946	41
6	0.019474	0.000286	0.007644	0.000132	(1, 1)	None	True	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.787709	0.803371	0.769663	0.758427	0.797753	0.783385	0.016946	41
36	0.025216	0.000498	0.008186	0.000234	(1, 2)	first	False	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.770950	0.797753	0.769663	0.758427	0.792135	0.777785	0.014779	45
42	0.025438	0.000616	0.008490	0.000167	(1, 2)	first	True	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.770950	0.797753	0.769663	0.758427	0.792135	0.777785	0.014779	45
12	0.019292	0.000265	0.007546	0.000222	(1, 1)	first	False	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.770950	0.797753	0.769663	0.758427	0.792135	0.777785	0.014779	45
18	0.019436	0.000436	0.007956	0.000396	(1, 1)	first	True	0.1	l1	{'columntransformer__countvectorizer__ngram_ra...	0.770950	0.797753	0.769663	0.758427	0.792135	0.777785	0.014779	45

\n", "

" ], "text/plain": [ " mean_fit_time std_fit_time ... std_test_score rank_test_score\n", "28 0.041511 0.003645 ... 0.025618 1\n", "46 0.052019 0.006278 ... 0.030171 2\n", "34 0.041211 0.004972 ... 0.024739 3\n", "40 0.051974 0.004799 ... 0.024998 4\n", "22 0.037861 0.004578 ... 0.023905 5\n", "10 0.030084 0.002975 ... 0.021520 6\n", "16 0.037340 0.004994 ... 0.024286 7\n", "4 0.030183 0.001353 ... 0.027533 8\n", "20 0.023119 0.001172 ... 0.021852 9\n", "44 0.029346 0.001732 ... 0.021852 9\n", "2 0.021188 0.000679 ... 0.019787 11\n", "47 0.028825 0.000462 ... 0.026265 12\n", "8 0.022105 0.000583 ... 0.026183 13\n", "38 0.028299 0.000755 ... 0.020194 14\n", "14 0.021598 0.000780 ... 0.020194 14\n", "11 0.023982 0.002240 ... 0.031065 16\n", "21 0.020800 0.000512 ... 0.024076 17\n", "3 0.020964 0.000622 ... 0.020141 18\n", "26 0.028059 0.000550 ... 0.022058 19\n", "23 0.023505 0.002672 ... 0.032182 20\n", "9 0.020925 0.000431 ... 0.023233 21\n", "15 0.020606 0.000601 ... 0.017107 22\n", "17 0.022542 0.000978 ... 0.024080 23\n", "35 0.028479 0.000384 ... 0.025357 23\n", "5 0.024417 0.001988 ... 0.024080 23\n", "32 0.029279 0.001644 ... 0.025773 26\n", "29 0.028205 0.000189 ... 0.020904 27\n", "45 0.027212 0.000554 ... 0.022143 28\n", "41 0.028515 0.001236 ... 0.019414 29\n", "39 0.027183 0.000146 ... 0.017164 30\n", "27 0.027390 0.000769 ... 0.018234 30\n", "33 0.027522 0.000263 ... 0.024489 32\n", "31 0.025574 0.000371 ... 0.015380 33\n", "7 0.020743 0.001260 ... 0.017253 34\n", "19 0.019558 0.000189 ... 0.017572 35\n", "43 0.026156 0.001185 ... 0.015849 36\n", "37 0.024954 0.000290 ... 0.016100 37\n", "25 0.025537 0.000881 ... 0.016944 38\n", "1 0.019058 0.000234 ... 0.016258 39\n", "13 0.018940 0.000372 ... 0.016343 40\n", "0 0.019134 0.000593 ... 0.016946 41\n", "30 0.024928 0.000464 ... 0.016946 41\n", "24 0.026180 0.001312 ... 0.016946 41\n", "6 0.019474 0.000286 ... 0.016946 41\n", "36 0.025216 0.000498 ... 0.014779 45\n", "42 0.025438 0.000616 ... 0.014779 45\n", "12 0.019292 0.000265 ... 0.014779 45\n", "18 0.019436 0.000436 ... 0.014779 45\n", "\n", "[48 rows x 18 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 103 } ] }, { "cell_type": "code", "metadata": { "id": "w319Fz0Me_Zt", "colab_type": "code", "outputId": "d125648c-fcc5-4603-a38c-a7d63488320f", "colab": { "base_uri": "https://localhost:8080/", "height": 36 } }, "source": [ "grid.best_score_" ], "execution_count": 104, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.8271357730211537" ] }, "metadata": { "tags": [] }, "execution_count": 104 } ] }, { "cell_type": "code", "metadata": { "id": "JpjufYj7aIhf", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 110 }, "outputId": "952904fd-5a9b-49ad-8043-1f5ec0d7e713" }, "source": [ "grid.best_params_" ], "execution_count": 105, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'columntransformer__countvectorizer__ngram_range': (1, 2),\n", " 'columntransformer__pipeline__onehotencoder__drop': None,\n", " 'columntransformer__simpleimputer__add_indicator': False,\n", " 'logisticregression__C': 10,\n", " 'logisticregression__penalty': 'l1'}" ] }, "metadata": { "tags": [] }, "execution_count": 105 } ] }, { "cell_type": "code", "metadata": { "id": "-iBtD2Tzfuta", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 372 }, "outputId": "7b21cff8-99b1-43d0-cc12-19ebc2590537" }, "source": [ "grid.predict(X_new) # The grid model will re-fit the data on best set of params" ], "execution_count": 106, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n", " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n", " 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n", " 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n", " 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n", " 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n", " 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n", " 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n", " 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n", " 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n", " 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n", " 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n", " 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n", " 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1])" ] }, "metadata": { "tags": [] }, "execution_count": 106 } ] }, { "cell_type": "code", "metadata": { "id": "5_6loAt4a45i", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }

	achem	adele	allen	berg	bradley	braund	briggs	cumings	elisabeth	florence	futrelle	gosta	harris	heath	heikkinen	henry	jacques	james	john	johnson	laina	leonard	lily	master	may	mccarthy	miss	moran	mr	mrs	nasser	nicholas	oscar	owen	palsson	peel	thayer	timothy	vilhelmina	william
0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
1	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0
2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
4	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0
7	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
8	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0
9	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0

	achem	adele	allen	berg	bradley	braund	briggs	cumings	elisabeth	florence	futrelle	gosta	harris	heath	heikkinen	henry	jacques	james	john	johnson	laina	leonard	lily	master	may	mccarthy	miss	moran	mr	mrs	nasser	nicholas	oscar	owen	palsson	peel	thayer	timothy	vilhelmina	william
0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
1	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0
2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
4	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0
7	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
8	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0
9	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0

	achem	adele	allen	berg	bradley	braund	briggs	cumings	elisabeth	florence	futrelle	gosta	harris	heath	heikkinen	henry	jacques	james	john	johnson	laina	leonard	lily	master	may	mccarthy	miss	moran	mr	mrs	nasser	nicholas	oscar	owen	palsson	peel	thayer	timothy	vilhelmina	william
0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
1	0	0	0	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0
2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
4	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0
7	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
8	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0
9	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0