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kljensen/samsung.ipynb

Created October 18, 2013 19:40
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samsung.ipynb
{
"metadata": {
"name": ""
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samsungData = pd.read_csv('./data/samsung/samsungData.csv')\n",
"samsungData = samsungData.drop(['Unnamed: 0'], axis=1) # drop the index numbers in the dataset"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samsungData['activity'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"laying 1407\n",
"standing 1374\n",
"sitting 1286\n",
"walk 1226\n",
"walkup 1073\n",
"walkdown 986\n",
"dtype: int64"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samsungData.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
"Index: 8 entries, count to max\n",
"Columns: 562 entries, tBodyAcc-mean()-X to subject\n",
"dtypes: float64(562)\n",
"</pre>"
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"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 8 entries, count to max\n",
"Columns: 562 entries, tBodyAcc-mean()-X to subject\n",
"dtypes: float64(562)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samsungData['activity'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"laying 1407\n",
"standing 1374\n",
"sitting 1286\n",
"walk 1226\n",
"walkup 1073\n",
"walkdown 986\n",
"dtype: int64"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samsungData = samsungData.drop([u'subject'], axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"samsungData.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"(7352, 562)"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = samsungData\n",
"df['catActivity'] = pd.Categorical.from_array(df.activity)\n",
"df['catActivity'].head (5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"0 standing\n",
"1 standing\n",
"2 standing\n",
"3 standing\n",
"4 standing\n",
"Name: catActivity, dtype: object"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import StratifiedKFold\n",
"train, test = iter(StratifiedKFold(df['catActivity'])).next()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.iloc[train]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
"Int64Index: 4901 entries, 0 to 7351\n",
"Columns: 563 entries, tBodyAcc-mean()-X to catActivity\n",
"dtypes: float64(561), object(2)\n",
"</pre>"
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"prompt_number": 20,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 4901 entries, 0 to 7351\n",
"Columns: 563 entries, tBodyAcc-mean()-X to catActivity\n",
"dtypes: float64(561), object(2)"
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"cell_type": "code",
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"input": [
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"language": "python",
"metadata": {},
"outputs": [
{
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"prompt_number": 21,
"text": [
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u'fBodyAccJerk-bandsEnergy()-49,64.2', u'fBodyAccJerk-bandsEnergy()-1,24.2', u'fBodyAccJerk-bandsEnergy()-25,48.2', u'fBodyGyro-mean()-X', u'fBodyGyro-mean()-Y', u'fBodyGyro-mean()-Z', u'fBodyGyro-std()-X', u'fBodyGyro-std()-Y', u'fBodyGyro-std()-Z', u'fBodyGyro-mad()-X', u'fBodyGyro-mad()-Y', u'fBodyGyro-mad()-Z', u'fBodyGyro-max()-X', u'fBodyGyro-max()-Y', u'fBodyGyro-max()-Z', u'fBodyGyro-min()-X', u'fBodyGyro-min()-Y', u'fBodyGyro-min()-Z', u'fBodyGyro-sma()', u'fBodyGyro-energy()-X', u'fBodyGyro-energy()-Y', u'fBodyGyro-energy()-Z', u'fBodyGyro-iqr()-X', u'fBodyGyro-iqr()-Y', u'fBodyGyro-iqr()-Z', u'fBodyGyro-entropy()-X', u'fBodyGyro-entropy()-Y', u'fBodyGyro-entropy()-Z', u'fBodyGyro-maxInds-X', u'fBodyGyro-maxInds-Y', u'fBodyGyro-maxInds-Z', u'fBodyGyro-meanFreq()-X', u'fBodyGyro-meanFreq()-Y', u'fBodyGyro-meanFreq()-Z', u'fBodyGyro-skewness()-X', u'fBodyGyro-kurtosis()-X', u'fBodyGyro-skewness()-Y', u'fBodyGyro-kurtosis()-Y', u'fBodyGyro-skewness()-Z', u'fBodyGyro-kurtosis()-Z', u'fBodyGyro-bandsEnergy()-1,8', u'fBodyGyro-bandsEnergy()-9,16', u'fBodyGyro-bandsEnergy()-17,24', u'fBodyGyro-bandsEnergy()-25,32', u'fBodyGyro-bandsEnergy()-33,40', u'fBodyGyro-bandsEnergy()-41,48', u'fBodyGyro-bandsEnergy()-49,56', u'fBodyGyro-bandsEnergy()-57,64', u'fBodyGyro-bandsEnergy()-1,16', u'fBodyGyro-bandsEnergy()-17,32', u'fBodyGyro-bandsEnergy()-33,48', u'fBodyGyro-bandsEnergy()-49,64', u'fBodyGyro-bandsEnergy()-1,24', u'fBodyGyro-bandsEnergy()-25,48', u'fBodyGyro-bandsEnergy()-1,8.1', u'fBodyGyro-bandsEnergy()-9,16.1', u'fBodyGyro-bandsEnergy()-17,24.1', u'fBodyGyro-bandsEnergy()-25,32.1', u'fBodyGyro-bandsEnergy()-33,40.1', u'fBodyGyro-bandsEnergy()-41,48.1', u'fBodyGyro-bandsEnergy()-49,56.1', u'fBodyGyro-bandsEnergy()-57,64.1', u'fBodyGyro-bandsEnergy()-1,16.1', u'fBodyGyro-bandsEnergy()-17,32.1', u'fBodyGyro-bandsEnergy()-33,48.1', u'fBodyGyro-bandsEnergy()-49,64.1', u'fBodyGyro-bandsEnergy()-1,24.1', u'fBodyGyro-bandsEnergy()-25,48.1', u'fBodyGyro-bandsEnergy()-1,8.2', u'fBodyGyro-bandsEnergy()-9,16.2', u'fBodyGyro-bandsEnergy()-17,24.2', u'fBodyGyro-bandsEnergy()-25,32.2', u'fBodyGyro-bandsEnergy()-33,40.2', u'fBodyGyro-bandsEnergy()-41,48.2', u'fBodyGyro-bandsEnergy()-49,56.2', u'fBodyGyro-bandsEnergy()-57,64.2', u'fBodyGyro-bandsEnergy()-1,16.2', u'fBodyGyro-bandsEnergy()-17,32.2', u'fBodyGyro-bandsEnergy()-33,48.2', u'fBodyGyro-bandsEnergy()-49,64.2', u'fBodyGyro-bandsEnergy()-1,24.2', u'fBodyGyro-bandsEnergy()-25,48.2', u'fBodyAccMag-mean()', u'fBodyAccMag-std()', u'fBodyAccMag-mad()', u'fBodyAccMag-max()', u'fBodyAccMag-min()', u'fBodyAccMag-sma()', u'fBodyAccMag-energy()', u'fBodyAccMag-iqr()', u'fBodyAccMag-entropy()', u'fBodyAccMag-maxInds', u'fBodyAccMag-meanFreq()', u'fBodyAccMag-skewness()', u'fBodyAccMag-kurtosis()', u'fBodyBodyAccJerkMag-mean()', u'fBodyBodyAccJerkMag-std()', u'fBodyBodyAccJerkMag-mad()', u'fBodyBodyAccJerkMag-max()', u'fBodyBodyAccJerkMag-min()', u'fBodyBodyAccJerkMag-sma()', u'fBodyBodyAccJerkMag-energy()', u'fBodyBodyAccJerkMag-iqr()', u'fBodyBodyAccJerkMag-entropy()', u'fBodyBodyAccJerkMag-maxInds', u'fBodyBodyAccJerkMag-meanFreq()', u'fBodyBodyAccJerkMag-skewness()', u'fBodyBodyAccJerkMag-kurtosis()', u'fBodyBodyGyroMag-mean()', u'fBodyBodyGyroMag-std()', u'fBodyBodyGyroMag-mad()', u'fBodyBodyGyroMag-max()', u'fBodyBodyGyroMag-min()', u'fBodyBodyGyroMag-sma()', u'fBodyBodyGyroMag-energy()', u'fBodyBodyGyroMag-iqr()', u'fBodyBodyGyroMag-entropy()', u'fBodyBodyGyroMag-maxInds', u'fBodyBodyGyroMag-meanFreq()', u'fBodyBodyGyroMag-skewness()', u'fBodyBodyGyroMag-kurtosis()', u'fBodyBodyGyroJerkMag-mean()', u'fBodyBodyGyroJerkMag-std()', u'fBodyBodyGyroJerkMag-mad()', u'fBodyBodyGyroJerkMag-max()', u'fBodyBodyGyroJerkMag-min()', u'fBodyBodyGyroJerkMag-sma()', u'fBodyBodyGyroJerkMag-energy()', u'fBodyBodyGyroJerkMag-iqr()', u'fBodyBodyGyroJerkMag-entropy()', u'fBodyBodyGyroJerkMag-maxInds', u'fBodyBodyGyroJerkMag-meanFreq()', u'fBodyBodyGyroJerkMag-skewness()', u'fBodyBodyGyroJerkMag-kurtosis()', u'angle(tBodyAccMean,gravity)', u'angle(tBodyAccJerkMean),gravityMean)', u'angle(tBodyGyroMean,gravityMean)', u'angle(tBodyGyroJerkMean,gravityMean)', u'angle(X,gravityMean)', u'angle(Y,gravityMean)', u'angle(Z,gravityMean)', u'activity', u'catActivity'], dtype=object)"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"features = df.columns[:561]\n",
"clf = RandomForestClassifier(n_estimators = 300, n_jobs=-1)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res = clf.fit(df[features].iloc[train], df['catActivity'].iloc[train])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"preds = clf.predict(df[features].iloc[test])\n",
"pd.crosstab(df['catActivity'].iloc[test], preds, rownames=['actual'], colnames=['predicted'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>predicted</th>\n",
" <th>laying</th>\n",
" <th>sitting</th>\n",
" <th>standing</th>\n",
" <th>walk</th>\n",
" <th>walkdown</th>\n",
" <th>walkup</th>\n",
" </tr>\n",
" <tr>\n",
" <th>actual</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>laying</th>\n",
" <td> 469</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>sitting</th>\n",
" <td> 0</td>\n",
" <td> 416</td>\n",
" <td> 13</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>standing</th>\n",
" <td> 0</td>\n",
" <td> 12</td>\n",
" <td> 446</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>walk</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 405</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>walkdown</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 325</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>walkup</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 357</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"predicted laying sitting standing walk walkdown walkup\n",
"actual \n",
"laying 469 0 0 0 0 0\n",
"sitting 0 416 13 0 0 0\n",
"standing 0 12 446 0 0 0\n",
"walk 0 0 0 405 3 1\n",
"walkdown 0 0 0 2 325 1\n",
"walkup 0 0 0 0 1 357"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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