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PCA.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import mne\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"import h5py\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mean_absolute_percentage_error(y_true, y_pred):\n",
" return np.mean(np.mean(np.abs(y_true - y_pred), axis = 0) / np.abs(np.mean(y_true, axis = 0))) * 100"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting parameters from ./data/resting_state/pre_egg_petuhova3103.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602749 = 0.000 ... 602.749 secs...\n",
"Extracting parameters from ./data/resting_state/2205_miloslavov_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 623249 = 0.000 ... 623.249 secs...\n",
"Extracting parameters from ./data/resting_state/kozunova_pre_eeg_2103.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602849 = 0.000 ... 602.849 secs...\n",
"Extracting parameters from ./data/resting_state/2505_shirokova_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 606599 = 0.000 ... 606.599 secs...\n",
"Extracting parameters from ./data/resting_state/gorbacheva_03021300_rest_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 625249 = 0.000 ... 625.249 secs...\n",
"Extracting parameters from ./data/resting_state/200317_ivanova_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602099 = 0.000 ... 602.099 secs...\n",
"Extracting parameters from ./data/resting_state/2403_kutuzova_posteeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604199 = 0.000 ... 604.199 secs...\n",
"Extracting parameters from ./data/resting_state/3103_petuhova_posteeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 630049 = 0.000 ... 630.049 secs...\n",
"Extracting parameters from ./data/resting_state/2103_glebko_posteeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 633899 = 0.000 ... 633.899 secs...\n",
"Extracting parameters from ./data/resting_state/glebko_2103_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 643749 = 0.000 ... 643.749 secs...\n",
"Extracting parameters from ./data/resting_state/gorin_310117_rest_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 609999 = 0.000 ... 609.999 secs...\n",
"Extracting parameters from ./data/resting_state/zavrib_post_eeg_eyesopen15021500.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 321599 = 0.000 ... 321.599 secs...\n",
"Extracting parameters from ./data/resting_state/gorbacheva_03_02_2017_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603949 = 0.000 ... 603.949 secs...\n",
"Extracting parameters from ./data/resting_state/post_eeg_tsoy_2504.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604949 = 0.000 ... 604.949 secs...\n",
"Extracting parameters from ./data/resting_state/2403_kutuzova_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602849 = 0.000 ... 602.849 secs...\n",
"Extracting parameters from ./data/resting_state/2505_shirokova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604799 = 0.000 ... 604.799 secs...\n",
"Extracting parameters from ./data/resting_state/dagaev_post_rest_eeg_30011600.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603799 = 0.000 ... 603.799 secs...\n",
"Extracting parameters from ./data/resting_state/zagirova_pre_eeg_2704.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 607549 = 0.000 ... 607.549 secs...\n",
"Extracting parameters from ./data/resting_state/08021400_post_eeg_shuhova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 607199 = 0.000 ... 607.199 secs...\n",
"Extracting parameters from ./data/resting_state/2704_zagirova_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604549 = 0.000 ... 604.549 secs...\n",
"Extracting parameters from ./data/resting_state/shuhova_08022017_rest_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 637949 = 0.000 ... 637.949 secs...\n",
"Extracting parameters from ./data/resting_state/zavrin_open_eyes_eeg_15021500.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 301999 = 0.000 ... 301.999 secs...\n",
"Extracting parameters from ./data/resting_state/zavrin_eyes_closed_eeg_15021500.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 302749 = 0.000 ... 302.749 secs...\n",
"Extracting parameters from ./data/resting_state/tsoy_pre_eeg_2504.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 652699 = 0.000 ... 652.699 secs...\n",
"Extracting parameters from ./data/resting_state/2003_ivanova_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 616599 = 0.000 ... 616.599 secs...\n",
"Extracting parameters from ./data/resting_state/bagaeva_post_eeg_13031500.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603899 = 0.000 ... 603.899 secs...\n",
"Extracting parameters from ./data/resting_state/gorin_rest_eeg_post_31011200.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603699 = 0.000 ... 603.699 secs...\n",
"Extracting parameters from ./data/resting_state/bagaeva_13021500_rest_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603949 = 0.000 ... 603.949 secs...\n",
"Extracting parameters from ./data/resting_state/miloslavov_22_05_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603399 = 0.000 ... 603.399 secs...\n",
"Extracting parameters from ./data/resting_state/300120171600_dagaev_rest_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 615599 = 0.000 ... 615.599 secs...\n",
"Extracting parameters from ./data/resting_state/zavrin_15021500_eyesclosed_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 319299 = 0.000 ... 319.299 secs...\n",
"Extracting parameters from ./data/resting_state/2103_kozunova_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603199 = 0.000 ... 603.199 secs...\n"
]
}
],
"source": [
"train_eeg_dir = \"./data/resting_state/\"\n",
"train_eeg_names = [x for x in os.listdir(train_eeg_dir) \n",
" if x[-5:] == \".vhdr\"]\n",
"\n",
"\n",
"data = np.zeros((0, 61))\n",
"\n",
"for eeg_name in train_eeg_names:\n",
" eeg_data = mne.io.read_raw_brainvision(train_eeg_dir + eeg_name, preload=True).get_data().T\n",
" data = np.append(data, eeg_data, axis = 0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(16569900, 61)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train, X_test, _, _ = train_test_split(data, data, test_size=0.1, random_state = 123)\n",
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pca = PCA()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
" svd_solver='auto', tol=0.0, whiten=False)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca.fit(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([8.60501206e-01, 4.50064918e-02, 1.96728939e-02, 1.32136598e-02,\n",
" 1.04975082e-02, 8.72080965e-03, 7.62335116e-03, 6.18126786e-03,\n",
" 5.67043513e-03, 4.47648307e-03, 3.60945585e-03, 3.14652398e-03,\n",
" 2.39228930e-03, 1.80131636e-03, 1.70855324e-03, 1.02205207e-03,\n",
" 8.56411536e-04, 7.16579942e-04, 5.79405287e-04, 4.55820757e-04,\n",
" 3.82103135e-04, 3.16569681e-04, 2.85133222e-04, 2.29655140e-04,\n",
" 2.04059298e-04, 1.75203243e-04, 1.37317351e-04, 1.10815493e-04,\n",
" 1.07661772e-04, 6.06199959e-05, 4.45915540e-05, 3.02155660e-05,\n",
" 1.35079571e-05, 1.01596334e-05, 8.84664590e-06, 5.20703831e-06,\n",
" 4.73173173e-06, 3.48769419e-06, 2.89469779e-06, 2.04873226e-06,\n",
" 1.96094774e-06, 1.75111927e-06, 1.46673460e-06, 1.33033908e-06,\n",
" 1.07091778e-06, 9.55877007e-07, 7.52438532e-07, 5.92499937e-07,\n",
" 5.42740492e-07, 4.09476932e-07, 3.70516426e-07, 3.20656854e-07,\n",
" 2.61712216e-07, 1.94464044e-07, 1.73691897e-07, 1.37400176e-07,\n",
" 1.18904211e-07, 9.71608336e-08, 7.81569206e-08, 6.95013218e-08,\n",
" 6.41651813e-33])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca.explained_variance_ratio_ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variance_ratio\n",
"\n",
"90 % информации - в первых 2 координатах \n",
"\n",
"95 % информации - в первых 6\n",
"\n",
"98 % информации - в первых 10\n",
"\n",
"99% информации - в первых 13"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eeg_dir = \"./data/new_data/\"\n",
"eeg_names = [x for x in os.listdir(eeg_dir) \n",
" if x[-5:] == \".vhdr\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting parameters from ./data/new_data/0311_pre_eeg_popova.vhdr...\n",
"Setting channel info structure...\n",
"Extracting parameters from ./data/new_data/2509_post_eeg_rinata.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 601549 = 0.000 ... 601.549 secs...\n",
"Extracting parameters from ./data/new_data/2510_post_eeg_osipova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602949 = 0.000 ... 602.949 secs...\n",
"Extracting parameters from ./data/new_data/0311_post_eeg_mazurova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 603349 = 0.000 ... 603.349 secs...\n",
"Extracting parameters from ./data/new_data/3111_post_eeg_popova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 612499 = 0.000 ... 612.499 secs...\n",
"Extracting parameters from ./data/new_data/1909_pre_eeg_eliseev.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 608399 = 0.000 ... 608.399 secs...\n",
"Extracting parameters from ./data/new_data/14071200_limanskaya_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604549 = 0.000 ... 604.549 secs...\n",
"Extracting parameters from ./data/new_data/3010_pre_eeg_suhareva.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 609899 = 0.000 ... 609.899 secs...\n",
"Extracting parameters from ./data/new_data/2809_pre_eeg_lukyanov.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 615899 = 0.000 ... 615.899 secs...\n",
"Extracting parameters from ./data/new_data/1207_shevchenko_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602849 = 0.000 ... 602.849 secs...\n",
"Extracting parameters from ./data/new_data/0708_pre_eeg_krasovskaya.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604049 = 0.000 ... 604.049 secs...\n",
"Extracting parameters from ./data/new_data/1207_shevchenko.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2132749 = 0.000 ... 2132.749 secs...\n",
"Extracting parameters from ./data/new_data/0311_eeg_mazurova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2048799 = 0.000 ... 2048.799 secs...\n",
"Extracting parameters from ./data/new_data/3010_post_eeg_suhareva.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 605799 = 0.000 ... 605.799 secs...\n",
"Extracting parameters from ./data/new_data/2809_lukyanov.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2085449 = 0.000 ... 2085.449 secs...\n",
"Extracting parameters from ./data/new_data/1608_post_eeg_polshina.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 632899 = 0.000 ... 632.899 secs...\n",
"Extracting parameters from ./data/new_data/17_11_pre_eeg_bachurina.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602149 = 0.000 ... 602.149 secs...\n",
"Extracting parameters from ./data/new_data/3110_maistrenko.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1914399 = 0.000 ... 1914.399 secs...\n",
"Extracting parameters from ./data/new_data/1509_pre_eeg_zvyagincev.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 606799 = 0.000 ... 606.799 secs...\n",
"Extracting parameters from ./data/new_data/2306_patrakeeva_post_eeg_rest.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 605199 = 0.000 ... 605.199 secs...\n",
"Extracting parameters from ./data/new_data/3010_eeg_suhareva.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1572349 = 0.000 ... 1572.349 secs...\n",
"Extracting parameters from ./data/new_data/1909_eliseev.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2093299 = 0.000 ... 2093.299 secs...\n",
"Extracting parameters from ./data/new_data/1909_post_eeg_eliseev.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 842249 = 0.000 ... 842.249 secs...\n",
"Extracting parameters from ./data/new_data/0307_STRELKOVA.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2103349 = 0.000 ... 2103.349 secs...\n",
"Extracting parameters from ./data/new_data/3110_post_eeg_maistrenko.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 620899 = 0.000 ... 620.899 secs...\n",
"Extracting parameters from ./data/new_data/2606_patrakeeva_main_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2106049 = 0.000 ... 2106.049 secs...\n",
"Extracting parameters from ./data/new_data/1608_rpre_eeg_polshina.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 629449 = 0.000 ... 629.449 secs...\n",
"Extracting parameters from ./data/new_data/2510_pre_eeg_osipova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604099 = 0.000 ... 604.099 secs...\n",
"Extracting parameters from ./data/new_data/0311_eeg_popova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1977699 = 0.000 ... 1977.699 secs...\n",
"Extracting parameters from ./data/new_data/0708_post_eeg_krasovskaya.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 614349 = 0.000 ... 614.349 secs...\n",
"Extracting parameters from ./data/new_data/14071200_limaskaya_post_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604849 = 0.000 ... 604.849 secs...\n",
"Extracting parameters from ./data/new_data/2510_osipova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1958699 = 0.000 ... 1958.699 secs...\n",
"Extracting parameters from ./data/new_data/2809_post_eeg_lukyanov.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602149 = 0.000 ... 602.149 secs...\n",
"Extracting parameters from ./data/new_data/1207_shevchenko_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 614199 = 0.000 ... 614.199 secs...\n",
"Extracting parameters from ./data/new_data/0708_eeg_krasovskaya.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2023149 = 0.000 ... 2023.149 secs...\n",
"Extracting parameters from ./data/new_data/2306_patrakeeva_pre_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Extracting parameters from ./data/new_data/0307_POST_EEG_sTRELKOVA.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 602149 = 0.000 ... 602.149 secs...\n",
"Extracting parameters from ./data/new_data/2509_eeg_rinata.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2129299 = 0.000 ... 2129.299 secs...\n",
"Extracting parameters from ./data/new_data/2509-pre_eeg_rinata.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 724899 = 0.000 ... 724.899 secs...\n",
"Extracting parameters from ./data/new_data/17_11_post_eeg_bachurina.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 610499 = 0.000 ... 610.499 secs...\n",
"Extracting parameters from ./data/new_data/1608_eeg_polshina.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1961799 = 0.000 ... 1961.799 secs...\n",
"Extracting parameters from ./data/new_data/311_pre_eeg_mazurova.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 604349 = 0.000 ... 604.349 secs...\n",
"Extracting parameters from ./data/new_data/1509_zvyagincev.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1910849 = 0.000 ... 1910.849 secs...\n",
"Extracting parameters from ./data/new_data/1407_limanskaya_eeg.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 2406349 = 0.000 ... 2406.349 secs...\n",
"Extracting parameters from ./data/new_data/3110_pre_eeg_maistrenko.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 663199 = 0.000 ... 663.199 secs...\n",
"Extracting parameters from ./data/new_data/17_11_bachurina.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 1979549 = 0.000 ... 1979.549 secs...\n",
"Extracting parameters from ./data/new_data/0307_STRELKOVA_PRE_EEG.vhdr...\n",
"Setting channel info structure...\n",
"Reading 0 ... 601999 = 0.000 ... 601.999 secs...\n"
]
}
],
"source": [
"test_data = np.zeros((0,61))\n",
"\n",
"fail_count = 0\n",
"\n",
"for eeg_name in eeg_names:\n",
" try:\n",
" eeg_data = mne.io.read_raw_brainvision(eeg_dir + eeg_name, preload=True).get_data().T\n",
" test_data = np.append(test_data, eeg_data, axis = 0)\n",
" except FileNotFoundError:\n",
" fail_count += 1\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"components_count = [1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 40, 45, 50, 55, 61]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"944.3816370159668\n",
"730.3861574688534\n",
"513.8760089270451\n",
"252.50333132341444\n",
"2.887346416340305e-12\n"
]
}
],
"source": [
"for component in components_count:\n",
" pca = PCA(n_components= component)\n",
" pca.fit(X_train)\n",
" mpe = mean_absolute_percentage_error(test_data, pca.inverse_transform(pca.transform(test_data)))\n",
" print(mpe)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[942.0808953154711,\n",
" 872.3344151492266,\n",
" 874.9637985353154,\n",
" 889.6565415207315,\n",
" 907.8391569944413,\n",
" 909.1332226860354,\n",
" 884.1113909588582,\n",
" 887.8328095567409,\n",
" 881.3496461942931,\n",
" 925.4499352925958,\n",
" 944.3816370159668,\n",
" 730.3861574688534,\n",
" 513.8760089270451,\n",
" 252.50333132341444,\n",
" 2.887346416340305e-12]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mpe_vars"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0016314035042201565\n",
"0.0016102983279754294\n",
"0.0016019980084192942\n",
"0.0015971624333699828\n",
"0.0015894735352078237\n",
"0.0015882914363946757\n",
"0.0015719374634677284\n",
"0.001565565903113536\n",
"0.0015613648299206107\n",
"0.0015451522477676401\n",
"0.001346346235438015\n",
"0.0012655422074663765\n",
"0.0011972963641907223\n",
"0.0011434864579471342\n",
"1.6767685391527792e-33\n"
]
}
],
"source": [
"mse_vars = []\n",
"for component in components_count:\n",
" pca = PCA(n_components= component)\n",
" pca.fit(X_train)\n",
" mse = mean_squared_error(test_data, pca.inverse_transform(pca.transform(test_data)))\n",
" print(mse)\n",
" mse_vars.append(mse)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.0016314035042201565,\n",
" 0.0016102983279754294,\n",
" 0.0016019980084192942,\n",
" 0.0015971624333699828,\n",
" 0.0015894735352078237,\n",
" 0.0015882914363946757,\n",
" 0.0015719374634677284,\n",
" 0.001565565903113536,\n",
" 0.0015613648299206107,\n",
" 0.0015451522477676401,\n",
" 0.001346346235438015,\n",
" 0.0012655422074663765,\n",
" 0.0011972963641907223,\n",
" 0.0011434864579471342,\n",
" 1.6767685391527792e-33]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mse_vars"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"components_count = [1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 40, 45, 50, 55, 61]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sb"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
{
"data": {
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VqbS0VKWlpYqLi9PPfvYzFRcXt9rGZDLJZDJd9mPEx8coMtLsb6myWOL83kdnRw/bB330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\nAP8f9cbYfMPApCwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fab778d9710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.array(components_count) / 61.\n",
"y = np.array(mpe_vars)\n",
"plt.scatter(x, y, label = \"PCA\")\n",
"plt.title(\"MAPE vs Alpha\")\n",
"plt.xlabel(\"Alpha\")\n",
"plt.ylabel(\"MAPE\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/anaconda3/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
{
"data": {
"image/png": 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bzeaxT1lZWaM+hYWFGjBggHr37u1edv7radOmad68ea3to6qqalvt05qoqDBV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\nRAh+AABMhOAHAMBE/j9IekHRGc9nWwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f076c3869b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.array(components_count) / 61.\n",
"y = np.array(mse_vars) * 100\n",
"plt.scatter(x, y, label = \"PCA\")\n",
"plt.title(\"MSE vs Alpha\")\n",
"plt.xlabel(\"Alpha\")\n",
"plt.ylabel(\"MSE\")\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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