Adds BCI results to staging
This commit is contained in:
mathew
Родитель
85edfe7c87
Коммит
55567a8bb4
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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@ -1,235 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"from sklearn.pipeline import Pipeline, FeatureUnion\n",
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"\n",
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"from sklearn.model_selection import cross_val_score, cross_val_predict\n",
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"from xgboost import XGBClassifier\n",
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"import numpy as np\n",
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"import itertools\n",
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"import seaborn\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from experiments.libs import loaders\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"\n",
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"from sklearn.svm import LinearSVC\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from xgboost import XGBModel\n",
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"\n",
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"from lightgbm import LGBMClassifier\n",
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"\n",
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"import mne\n",
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"from scipy.io import loadmat\n",
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"from libs.loaders import load_bci\n",
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"from itertools import zip_longest\n",
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"from copy import deepcopy\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"pipeline_steps = [('scale', StandardScaler())]\n",
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"continuous_pipeline = Pipeline(steps=pipeline_steps)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"featurisers = [('continuous', continuous_pipeline)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"xgb_clf_pipeline = Pipeline(steps=[('features', FeatureUnion(featurisers)),\n",
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" ('clf', XGBClassifier(max_depth=2, \n",
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" learning_rate=0.1, \n",
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" scale_pos_weight=2,\n",
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" n_estimators=100,\n",
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" gamma=0.1,\n",
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" subsample=1))]) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"lgbm_clf_pipeline = Pipeline(steps=[('features', FeatureUnion(featurisers)),\n",
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" ('clf', LGBMClassifier(num_leaves=2**2,\n",
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" learning_rate=0.1, \n",
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" scale_pos_weight=2,\n",
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" n_estimators=100,\n",
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" subsample=1))]) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"X, y, X_test, y_test = load_bci()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 1min 38s, sys: 688 ms, total: 1min 39s\n",
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"Wall time: 5.59 s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Pipeline(steps=[('features', FeatureUnion(n_jobs=1,\n",
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" transformer_list=[('continuous', Pipeline(steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True))]))],\n",
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" transformer_weights=None)), ('clf', XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n",
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" gamma=0...logistic', reg_alpha=0, reg_lambda=1,\n",
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" scale_pos_weight=2, seed=0, silent=True, subsample=1))])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"xgb_clf_pipeline.fit(X, y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.68251896814231094"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y_pred = xgb_clf_pipeline.predict_proba(X_test)\n",
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"roc_auc_score(y_test, y_pred[:, 1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 17.3 s, sys: 168 ms, total: 17.5 s\n",
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"Wall time: 2.4 s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Pipeline(steps=[('features', FeatureUnion(n_jobs=1,\n",
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" transformer_list=[('continuous', Pipeline(steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True))]))],\n",
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" transformer_weights=None)), ('clf', LGBMClassifier(boosting_type='gbdt', colsample_bytree=1, drop_rate=0.1,\n",
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" is_un... subsample_for_bin=50000, subsample_freq=1, uniform_drop=False,\n",
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" xgboost_dart_mode=False))])"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"lgbm_clf_pipeline.fit(X, y)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -1,95 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Playground\n",
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"\n",
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"Playground notebook to quickly test code."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"System version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:53:06) \n",
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"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n"
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]
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}
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],
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"source": [
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"import os,sys\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from lightgbm.sklearn import LGBMRegressor\n",
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"import xgboost as xgb\n",
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"\n",
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"from libs.timer import Timer\n",
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"\n",
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"print(\"System version: {}\".format(sys.version))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.000576000000000132\n"
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]
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}
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],
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"source": [
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"with Timer() as t:\n",
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" for i in range(10000):\n",
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" r = 1\n",
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"print(t.interval)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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