-
-
Save cindithompson/46ad7fd2cc11dbb489f956c7b0ccdb10 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Python and data setup" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import os\n", | |
"\n", | |
"import numpy as np\n", | |
"import pandas as pd\n", | |
"import matplotlib.pyplot as plt\n", | |
"from matplotlib import rcParams\n", | |
"from skimage import io\n", | |
"import sklearn\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"rcParams['font.size'] = 14\n", | |
"rcParams['lines.linewidth'] = 2\n", | |
"rcParams['figure.figsize'] = (10, 6)\n", | |
"rcParams['axes.titlepad'] = 20" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"PLANET_KAGGLE_ROOT = os.path.abspath(\"input/\")\n", | |
"PLANET_KAGGLE_JPEG_DIR = os.path.join(PLANET_KAGGLE_ROOT, 'train-jpg')\n", | |
"PLANET_KAGGLE_LABEL_CSV = os.path.join(PLANET_KAGGLE_ROOT, 'train_v2.csv')\n", | |
"assert os.path.exists(PLANET_KAGGLE_ROOT)\n", | |
"assert os.path.exists(PLANET_KAGGLE_JPEG_DIR)\n", | |
"assert os.path.exists(PLANET_KAGGLE_LABEL_CSV)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"labels_df = pd.read_csv(PLANET_KAGGLE_LABEL_CSV)\n", | |
"# Build list with unique labels\n", | |
"label_list = set()\n", | |
"for tag_str in labels_df.tags.values:\n", | |
" labels = tag_str.split()\n", | |
" for label in labels:\n", | |
" label_list.add(label)\n", | |
"# Add one hot features (new columns in the dataframe) for every label\n", | |
"for label in label_list:\n", | |
" labels_df[label] = labels_df['tags'].apply(lambda x: 1 if label in x.split(' ') else 0)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"label_list = list(label_list)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Split up the training data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"num_exs = len(labels_df)\n", | |
"ntrain = int(num_exs * .6)\n", | |
"nval = int((num_exs-ntrain)/2)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 30, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"24287\n", | |
"8096\n" | |
] | |
} | |
], | |
"source": [ | |
"print(ntrain)\n", | |
"print(nval)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# data is already shuffled by the contest organizers, no need to randomize\n", | |
"train_data = labels_df[:ntrain]\n", | |
"validation_data = labels_df[ntrain:ntrain+nval]\n", | |
"test_data = labels_df[ntrain+nval:]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Baseline model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"valid_true = validation_data[label_list] # get the actual labels for the validation set\n", | |
"# set up an empty prediction vector, length the number of classes\n", | |
"preds = np.zeros(len(label_list))\n", | |
"preds = pd.DataFrame([preds]*len(validation_data),columns=label_list)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# just for fun, I'm also showing score for random vector, and a vector of all 1's\n", | |
"rand_preds = np.random.choice([0,1], size=(len(validation_data),len(label_list)))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from sklearn.metrics import fbeta_score" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"0.33572727002015307" | |
] | |
}, | |
"execution_count": 15, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"fbeta_score(valid_true, rand_preds, beta=2, average='samples')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"one_preds = np.ones(len(label_list))\n", | |
"one_preds = pd.DataFrame([one_preds]*len(validation_data),columns=label_list)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"0.48279449258650137" | |
] | |
}, | |
"execution_count": 17, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"fbeta_score(valid_true, one_preds, beta=2, average='samples')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"valid_true = validation_data[label_list] # get the actual labels for the validation set\n", | |
"# set up an empty prediction vector, length the number of classes\n", | |
"preds = np.zeros(len(label_list))\n", | |
"# set the primary label to positive\n", | |
"preds[label_list.index('primary')] = 1" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# now populate a prediction matrix, every example just with the primary label\n", | |
"valid_baseline_pred = pd.DataFrame([preds]*len(validation_data),columns=label_list)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 20, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# we ought to do better saying clear & primary:\n", | |
"preds[label_list.index('clear')] = 1\n", | |
"valid_baseline_pred_2 = pd.DataFrame([preds]*len(validation_data),columns=label_list)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Evaluation Metric" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from sklearn.metrics import fbeta_score" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"0.4015255862310777" | |
] | |
}, | |
"execution_count": 22, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"fbeta_score(valid_true, valid_baseline_pred, beta=2, average='samples')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 23, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"0.6433861442943634" | |
] | |
}, | |
"execution_count": 23, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"fbeta_score(valid_true, valid_baseline_pred_2, beta=2, average='samples')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### A Smarter Model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 24, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from sklearn.multiclass import OneVsRestClassifier\n", | |
"from sklearn.linear_model import LogisticRegression\n", | |
"import cv2\n", | |
"\n", | |
"rescaled_dim = 32" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Started from https://www.kaggle.com/syedosman/logistic-regression-classification" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"The below code is a bit complex! It reads the jpg training images, rescales them down, and reshapes each to a flat vector for input to Sklearn, which requires numpy arrays!" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 25, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"X_train = np.squeeze(np.array([cv2.resize(io.imread(os.path.join(PLANET_KAGGLE_ROOT, 'train-jpg', name+'.jpg')),\n", | |
" (rescaled_dim, rescaled_dim), cv2.INTER_LINEAR).reshape(1, -1)\n", | |
" for name in train_data['image_name'].values]))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | |
" }\n", | |
"\n", | |
" .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | |
" }\n", | |
"\n", | |
" .dataframe thead th {\n", | |
" text-align: right;\n", | |
" }\n", | |
"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>conventional_mine</th>\n", | |
" <th>primary</th>\n", | |
" <th>habitation</th>\n", | |
" <th>water</th>\n", | |
" <th>road</th>\n", | |
" <th>blooming</th>\n", | |
" <th>blow_down</th>\n", | |
" <th>partly_cloudy</th>\n", | |
" <th>selective_logging</th>\n", | |
" <th>haze</th>\n", | |
" <th>clear</th>\n", | |
" <th>slash_burn</th>\n", | |
" <th>bare_ground</th>\n", | |
" <th>artisinal_mine</th>\n", | |
" <th>cultivation</th>\n", | |
" <th>agriculture</th>\n", | |
" <th>cloudy</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" conventional_mine primary habitation water road blooming blow_down \\\n", | |
"0 0 1 0 0 0 0 0 \n", | |
"1 0 1 0 1 0 0 0 \n", | |
"2 0 1 0 0 0 0 0 \n", | |
"3 0 1 0 0 0 0 0 \n", | |
"4 0 1 1 0 1 0 0 \n", | |
"\n", | |
" partly_cloudy selective_logging haze clear slash_burn bare_ground \\\n", | |
"0 0 0 1 0 0 0 \n", | |
"1 0 0 0 1 0 0 \n", | |
"2 0 0 0 1 0 0 \n", | |
"3 0 0 0 1 0 0 \n", | |
"4 0 0 0 1 0 0 \n", | |
"\n", | |
" artisinal_mine cultivation agriculture cloudy \n", | |
"0 0 0 0 0 \n", | |
"1 0 0 1 0 \n", | |
"2 0 0 0 0 \n", | |
"3 0 0 0 0 \n", | |
"4 0 0 1 0 " | |
] | |
}, | |
"execution_count": 26, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# now get the correct labels for the training data\n", | |
"y_train = train_data[label_list]\n", | |
"y_train.head()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 27, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# and the validation set\n", | |
"X_valid = np.squeeze(np.array([cv2.resize(io.imread(os.path.join(PLANET_KAGGLE_ROOT, 'train-jpg', name+'.jpg')),\n", | |
" (rescaled_dim, rescaled_dim), cv2.INTER_LINEAR).reshape(1, -1)\n", | |
" for name in validation_data['image_name'].values]))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"y_valid = validation_data[label_list]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Recall the discussion of normalization in the data preparation post" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 29, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"allXs = np.concatenate((X_train, X_valid))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 31, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/cynthiathompson/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by the scale function.\n", | |
" warnings.warn(msg, DataConversionWarning)\n" | |
] | |
} | |
], | |
"source": [ | |
"allXs = sklearn.preprocessing.scale(allXs)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"24287 8096\n" | |
] | |
} | |
], | |
"source": [ | |
"X_train = allXs[:ntrain]\n", | |
"X_valid = allXs[ntrain:]\n", | |
"print(len(X_train), len(X_valid))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from sklearn.multiclass import OneVsRestClassifier\n", | |
"from sklearn.linear_model import LogisticRegression" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 34, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", | |
" intercept_scaling=1, max_iter=100, multi_class='warn',\n", | |
" n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n", | |
" tol=0.0001, verbose=0, warm_start=False),\n", | |
" n_jobs=None)" | |
] | |
}, | |
"execution_count": 34, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"clf = OneVsRestClassifier(LogisticRegression(solver='liblinear'))\n", | |
"clf.fit(X_train, y_train)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 36, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/cynthiathompson/miniconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no predicted labels.\n", | |
" 'precision', 'predicted', average, warn_for)\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"0.6755013691546999" | |
] | |
}, | |
"execution_count": 36, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"pred_test = clf.predict(X_valid)\n", | |
"fbeta_score(y_valid, pred_test, beta=2, average='samples')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.7.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment