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2017-05-01 13:58:40 +08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook I use hyperoptimisation to find the best data filters and the best decision tree parameters."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-24T11:42:36.209833Z",
"start_time": "2017-03-24T19:42:34.670131+08:00"
}
},
"outputs": [],
"source": [
"from path import Path\n",
"import arrow\n",
"import json\n",
"import pytz\n",
"from pprint import pprint\n",
"from tqdm import tqdm_notebook as tqdm\n",
"import re, os, collections, itertools, uuid, logging\n",
"import tempfile\n",
"# import tables\n",
"\n",
"import zipfile\n",
"import urllib\n",
"\n",
"import ee\n",
"import pyproj\n",
"import numpy as np\n",
"import scipy as sp\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"import shapely\n",
"\n",
"\n",
"%matplotlib inline\n",
"# %precision 4\n",
"plt.style.use('fivethirtyeight')\n",
"plt.rcParams['figure.figsize'] = (12, 6) # bigger plots"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:18:02.082328Z",
"start_time": "2017-03-26T13:18:02.079838+08:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"from collections import OrderedDict\n",
"import tabulate\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-24T11:42:36.720162Z",
"start_time": "2017-03-24T19:42:36.717812+08:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"os.environ['CUDA_VISIBLE_DEVICES']=\"\" # don't need this for skleanr"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-24T11:42:38.402360Z",
"start_time": "2017-03-24T19:42:37.166038+08:00"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import keras\n",
"import tensorflow\n",
"import sklearn\n",
"from keras.utils.io_utils import HDF5Matrix\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import Reshape, InputLayer, Permute, RepeatVector, Dropout, LocallyConnected1D\n",
"from keras.layers.core import Activation\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.layers.convolutional import UpSampling1D\n",
"from keras.layers.convolutional import Convolution1D, MaxPooling1D, MaxPooling2D\n",
"from keras.layers.core import Flatten\n",
"from keras.optimizers import SGD\n",
"from keras.datasets import mnist\n",
"from keras.layers import (Input, LocallyConnected1D, ZeroPadding1D, Cropping1D, Embedding, Merge, merge,\n",
" Cropping2D, Convolution1D, Convolution2D, Deconvolution2D, BatchNormalization, UpSampling1D, RepeatVector)\n",
"from keras.layers import embeddings, convolutional, activations, normalization, advanced_activations, ZeroPadding2D, UpSampling2D\n",
"from keras.models import Model\n",
"from keras_tqdm import TQDMCallback, TQDMNotebookCallback"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-24T11:42:38.406410Z",
"start_time": "2017-03-24T19:42:38.404062+08:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"# %load_ext autoreload\n",
"# %autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-24T11:42:43.013567Z",
"start_time": "2017-03-24T19:42:39.350448+08:00"
}
},
"outputs": [],
"source": [
"# %load_ext autoreload\n",
"# %autoreload 2\n",
"\n",
"helper_dir = str(Path('.').abspath())\n",
"if helper_dir not in os.sys.path:\n",
" os.sys.path.append(helper_dir)\n",
" \n",
"from leak_helpers.earth_engine import display_ee, get_boundary, tifs2np, bands_s2, download_image, bands_s2\n",
"from leak_helpers.geometry import diffxy, resample_polygon\n",
"from leak_helpers.modelling import ImageDataGenerator, dice_coef_loss\n",
"from leak_helpers.visualization import imshow_bands\n",
"from leak_helpers.analysis import parse_classification_report, find_best_dummy_classification, calculate_result_class\n",
"from leak_helpers.modelling.filters import is_not_cloudy, is_not_center_cloudy, is_image_within, is_leak, filter_split_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-24T11:42:43.018865Z",
"start_time": "2017-03-24T19:42:43.015056+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"('1.2.2', '1.0.0')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"keras.__version__, tensorflow.__version__"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:19:29.430946Z",
"start_time": "2017-03-26T13:19:29.418263+08:00"
}
},
"outputs": [],
"source": [
"# params\n",
"\n",
"data_path = [\n",
"# Path('../data/scraped_satellite_images/s2-AUTX_v6_COPERNICUS-S2'),\n",
" Path('../data/scraped_satellite_images/l7-AUTX_v2_LANDSAT-LE7_L1T'),\n",
"# Path('../data/scraped_satellite_images/l8-AUTX_v2_LANDSAT-LC8_L1T'),\n",
"# Path('../data/scraped_satellite_images/s1-all_COPERNICUS-S1_GRD'), # small\n",
"][0]\n",
"\n",
"notebook_name = 'leak_detection_CNN'\n",
"batch_size=128\n",
"random_seed = 1337\n",
"test_fraction = 0.4\n",
"timespan_before = 60*60*24*2 # we will only take image that where X seconds before the leak (e.g. 1 day)\n",
"max_cloud_cover = 1.1 # cloud cover must less than this\n",
"thresh = 0.5 # balance precision vs recall\n",
"balanced_classes = False \n",
"normalized = True\n",
"\n",
"# derived\n",
"seconds_in_a_day = 60*60*24\n",
"ts=arrow.utcnow().format('YYYYMMDD-HH-mm-ss')\n",
"model_name = '{notebook_name:}__{ts:}'.format(ts=ts,notebook_name=notebook_name)\n",
"outdir = Path('../output').joinpath(model_name)\n",
"outdir.makedirs_p()\n",
"outdir\n",
"\n",
"target_names = ['no leak','leak']"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:19:33.440105Z",
"start_time": "2017-03-26T13:19:30.139255+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"((3564, 14, 25, 25),\n",
" array([False, False, False, ..., False, False, True], dtype=bool),\n",
" dict_keys(['distance', 'name', 'scale', 'leak', 'crs', 'image']))"
]
},
"execution_count": 303,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"script_metadata = json.load(open(data_path.joinpath('script_metadata.json')))\n",
"# test load\n",
"import h5py\n",
"metadatas = json.load(open(data_path.joinpath('data_metadata.json')))\n",
"with h5py.File(data_path.joinpath('data.h5'),'r') as h5f:\n",
" X_raw = h5f['X'][:]\n",
" y_raw = h5f['y'][:]\n",
"X_raw.shape, y_raw, metadatas[0].keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T09:47:54.194535Z",
"start_time": "2017-03-13T17:47:54.187303+08:00"
},
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:19:34.133300Z",
"start_time": "2017-03-26T13:19:33.441815+08:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(926, 14, 25, 25) (926,) 926\n",
"(883, 14, 25, 25) (883,) 883\n",
"(398, 14, 25, 25) (398,) 398\n"
]
}
],
"source": [
"X_train, y_train, metadata_train, X_val, y_val, metadata_val, X_test, y_test, metadata_test = filter_split_data(\n",
" X_raw,\n",
" y_raw,\n",
" metadatas,\n",
" max_cloud_cover=1.1, \n",
" timespan_before=timespan_before,\n",
" test_fraction=test_fraction, \n",
" random_seed=random_seed,\n",
" balanced_classes=balanced_classes,\n",
" normalized=normalized,\n",
" filter_center_cloudy=True,\n",
") \n",
"print(X_train.shape,y_train.shape, len(metadata_train))\n",
"print(X_test.shape,y_test.shape, len(metadata_test))\n",
"print(X_val.shape,y_val.shape, len(metadata_val))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-12T04:05:05.554070Z",
"start_time": "2017-03-12T12:05:05.550725+08:00"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# model\n",
"\n",
"- batch norm helped\n",
"- normalising X helped\n",
"\n",
"Refs:\n",
"- conv only net uses strides instead of MaxPooling\n",
"- example of heatmap generationg https://github.com/heuritech/convnets-keras/blob/master/convnetskeras/convnets.py"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:19:34.155906Z",
"start_time": "2017-03-26T13:19:34.134973+08:00"
},
"code_folding": []
},
"outputs": [],
"source": [
"# conv model\n",
"\n",
"def get_model(input_shape, batch_norm=True, activation='relu', border_mode='same', channels = 14*2, pooling = 2): \n",
" \n",
" model = Sequential()\n",
" # batch_input_shape=(None, X_train.shape[1], pixel_length, pixel_length)\n",
" model.add(InputLayer(input_shape=input_shape, name='input'))\n",
" model.add(Permute((2,3,1)))\n",
"\n",
" model.add(Convolution2D(channels,3,3,border_mode=border_mode,subsample=(pooling, pooling)))\n",
" if batch_norm: model.add(BatchNormalization())\n",
" model.add(Activation(activation))\n",
"\n",
" model.add(Convolution2D(channels*3,3,3,border_mode=border_mode,subsample=(3, 3)))\n",
" if batch_norm: model.add(BatchNormalization())\n",
" model.add(Activation(activation))\n",
"\n",
" model.add(Convolution2D(channels*2*3,3,3,border_mode=border_mode,subsample=(pooling, pooling)))\n",
" if batch_norm: model.add(BatchNormalization())\n",
" model.add(Activation(activation))\n",
"\n",
" model.add(Convolution2D(channels*2*3*2,1,1,border_mode=border_mode,subsample=(pooling, pooling)))\n",
" if batch_norm: model.add(BatchNormalization())\n",
" model.add(Activation(activation))\n",
" \n",
" model.add(Convolution2D(1,1,1,border_mode=border_mode))\n",
" model.add(Flatten())\n",
" model.add(Activation('sigmoid'))\n",
" \n",
" model.compile(loss='binary_crossentropy',optimizer='nadam', metrics=['accuracy'])\n",
" \n",
" return model\n",
"\n",
"input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3])\n",
"# model = get_model(input_shape, border_mode='same' ,batch_norm=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-07T07:32:33.915919Z",
"start_time": "2017-03-07T15:32:33.893877+08:00"
}
},
"source": [
"# Hyperopt"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-12T02:24:36.259912Z",
"start_time": "2017-03-12T10:24:36.250787+08:00"
}
},
"source": [
"## hyperopt nn"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:19:34.261396Z",
"start_time": "2017-03-26T13:19:34.157906+08:00"
},
"code_folding": [
0
]
},
"outputs": [],
"source": [
"# hypropt nn\n",
"from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
"from sklearn.metrics import roc_auc_score\n",
"import sys\n",
"from sklearn.dummy import DummyClassifier\n",
"\n",
"space = {\n",
" \n",
" # model\n",
" 'batch_norm': hp.choice('batch_norm', [False, True]),\n",
" 'activation':hp.choice('batch_norm', ['relu','tanh','hard_sigmoid','sigmoid']),\n",
" 'channels': 14*2,\n",
" 'pooling':2,\n",
" 'optimizer':'nadam',\n",
" \n",
" # data\n",
" 'max_cloud_cover':hp.uniform('max_cloud_cover', 0.01, 0.3),\n",
" 'timespan_before': seconds_in_a_day*1, # hp.uniform('timespan_before', seconds_in_a_day*1, seconds_in_a_day*7),\n",
" 'balanced_classes':hp.choice('balanced_classes', [False, True]),\n",
" 'normalized': hp.choice('normalized', [False, True]),\n",
" 'filter_center_cloudy': hp.choice('filter_center_cloudy', [False, True]),\n",
" \n",
" # std is 5% so max should prob be ~5%\n",
" 'channel_shift_range': 0.007, #hp.uniform('channel_shift_range', 0.0, 0.07),\n",
" 'rotation_range': 25, #hp.uniform('rotation_range', 0.0, 45),\n",
" \n",
" 'batch_size' : batch_size,\n",
" 'samples_per_epoch': len(X_raw)*4,\n",
" 'nb_epochs' : 40,\n",
" }\n",
"\n",
"# from keras.models import Sequential\n",
"# from keras.layers import InputLayer\n",
"# from keras.layers.core import Dense, Dropout, Activation\n",
"# from keras.optimizers import Adadelta, Adam, rmsprop\n",
"\n",
"# TODO make everything run of the params\n",
"# TODO encode params into hyperopt\n",
"# TODO run some trials to try differen't data types, could use decision tree, or even null model?\n",
"def f_nn(params): \n",
"\n",
" # Get data\n",
" X_train, y_train, metadata_train, X_val, y_val, metadata_val, X_test, y_test, metadata_test = filter_split_data(\n",
" X_raw,\n",
" y_raw,\n",
" metadatas,\n",
" max_cloud_cover=params['max_cloud_cover'], \n",
" timespan_before=params['timespan_before'],\n",
" test_fraction=test_fraction, \n",
" random_seed=random_seed,\n",
" balanced_classes=params['balanced_classes'],\n",
" normalized=params['normalized'],\n",
" filter_center_cloudy=params['filter_center_cloudy']\n",
" ) \n",
"\n",
" datagen = ImageDataGenerator(\n",
" # featurewise_center=True,\n",
" # featurewise_std_normalization=True,\n",
" channel_shift_range=params['channel_shift_range'],\n",
" rotation_range=params['rotation_range'],\n",
" # width_shift_range=0.05,\n",
" # height_shift_range=0.05,\n",
" horizontal_flip=True,\n",
" vertical_flip=True,\n",
" dim_ordering='th',\n",
" # rescale=0.02,\n",
" # zoom_range=0.05,\n",
" # shear_range=0.05,\n",
" )\n",
"\n",
" # datagen.fit(X_train)\n",
"\n",
" # Get model\n",
" input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3])\n",
" model = get_model(\n",
" input_shape, \n",
" border_mode=params['border_mode'],\n",
" batch_norm=params['batch_norm'],\n",
" channels=params['channels'],\n",
" pooling=params['pooling']\n",
" \n",
" )\n",
"\n",
" if balanced_classes:\n",
" model.compile(loss='binary_crossentropy',optimizer=params['optimizer'], metrics=['accuracy','matthews_correlation'])\n",
" else:\n",
" model.compile(loss=dice_coef_loss,optimizer=params['optimizer'], metrics=['accuracy','matthews_correlation'])\n",
"\n",
" # pretrain/test\n",
" history = model.fit_generator(\n",
" datagen.flow(X_train, y_train, batch_size=params['batch_size']),\n",
" samples_per_epoch=params['samples_per_epoch'],\n",
" verbose=0, \n",
" nb_epoch=params['nb_epoch'], \n",
" validation_data=[X_val,y_val],\n",
" callbacks=[\n",
" TQDMNotebookCallback(),\n",
" keras.callbacks.EarlyStopping(patience=6, monitor='loss'),\n",
" keras.callbacks.ReduceLROnPlateau(monitor='loss', patience=3, verbose=1)\n",
" ]\n",
" )\n",
"\n",
" # results\n",
" last_metrics = [x[-1] for x in history.history.values()]\n",
" last_metrics = dict(zip(history.history.keys(),last_metrics))\n",
"\n",
" # accuracy X_train\n",
" model.compile(loss='binary_crossentropy',optimizer='nadam', metrics=['accuracy','mean_squared_error','precision','recall','fbeta_score','fmeasure','matthews_correlation'])\n",
" score = model.evaluate(X_val,y_val, batch_size=batch_size, verbose=False)\n",
" metrics = dict(zip(model.metrics_names,score))\n",
"\n",
" # score on X_val\n",
" y_pred = model.predict(X_val, verbose=False).T[0]\n",
" report = parse_classification_report(sklearn.metrics.classification_report(y_val>thresh,y_pred>thresh,target_names=target_names))\n",
" loss=-metrics['matthews_correlation']\n",
" \n",
" X_train2 = X_train.reshape((-1,14*24*24))\n",
" X_val2 = X_val.reshape((-1,14*24*24))\n",
" \n",
" clf = DummyClassifier(strategy='uniform')\n",
" clf.fit(X_train2,y_train)\n",
" y_pred = clf.predict(X_val2)\n",
" score_dummy = clf.score(X_val2, y_val)\n",
" matthews_corrcoef_dummy = sklearn.metrics.matthews_corrcoef(y_val>thresh, y_pred>thresh)\n",
" report_dummy = parse_classification_report(sklearn.metrics.classification_report(y_val>thresh,y_pred>thresh,target_names=target_names))\n",
" \n",
" \n",
" \n",
" sys.stdout.flush() \n",
" return dict(\n",
" # core info\n",
" loss=-loss,\n",
" status=STATUS_OK,\n",
" \n",
" # info\n",
" history=history.history, \n",
" metrics=dict(\n",
" last_metrics=last_metrics,\n",
" report=report.to_dict(),\n",
" metrics=metrics,\n",
" ),\n",
" dummy_metrics=dict(\n",
" report_dummy=report_dummy.to_dict(),\n",
" score_dummy=score_dummy,\n",
" matthews_corrcoef_dummy=matthews_corrcoef_dummy,\n",
" ),\n",
" \n",
" # data dumps as json\n",
" attachments=dict(\n",
" model=model.to_json()\n",
" )\n",
" )\n",
" \n",
"\n",
"\n",
"# trials = Trials()\n",
"# best = fmin(f_nn, space, algo=tpe.suggest, max_evals=2, trials=trials, verbose=2)\n",
"# print ('best: ')\n",
"# print (best)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-10T06:30:44.245328Z",
"start_time": "2017-03-10T14:30:44.242000+08:00"
}
},
"source": [
"## hyperopt filter"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:19:34.343184Z",
"start_time": "2017-03-26T13:19:34.263426+08:00"
}
},
"outputs": [],
"source": [
"\"\"\"\n",
"This experiment will look at differen't way of filtering data\n",
"and will use decision trees as quick ways of evaluating data.\n",
"Mathews cooeffecient is used as loss since it performs well for unbalanced datasets.\n",
"\"\"\"\n",
"\n",
"from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
"from sklearn.metrics import roc_auc_score\n",
"from hyperopt.mongoexp import MongoTrials\n",
"import sys\n",
"from sklearn import tree\n",
"from sklearn.dummy import DummyClassifier\n",
"import time\n",
"seconds_in_a_day = 60*60*24\n",
"\n",
"trials = Trials(exp_key='data_filters_%s'%data_path.basename())\n",
"# trials = MongoTrials('mongo://localhost:27017/hyperopt_leaks2/jobs', exp_key='data_filters_v2')\n",
"\n",
"\n",
"\n",
"space = {\n",
"\n",
" 'max_cloud_cover': hp.uniform('max_cloud_cover', 0.01, 1.0),\n",
" 'timespan_before': hp.uniform('timespan_before', seconds_in_a_day*0.5, seconds_in_a_day*7),\n",
" 'balanced_classes':hp.choice('balanced_classes', [False, True]),\n",
" 'normalized': hp.choice('normalized', [False, True]),\n",
" 'filter_center_cloudy': False, #hp.choice('filter_center_cloudy', [False, True]),\n",
" \n",
" # std is 5% so max should prob be ~5%\n",
" 'channel_shift_range': hp.uniform('channel_shift_range', 0.0, 0.25),\n",
" 'rotation_range': hp.uniform('rotation_range', 0.0, 45),\n",
" 'width_shift_range': hp.uniform('width_shift_range', 0.0, 0.5),\n",
" 'height_shift_range': hp.uniform('height_shift_range', 0.0, 0.5),\n",
" 'horizontal_flip': True, #hp.choice('horizontal_flip', [False, True]),\n",
" 'vertical_flip': True, #hp.choice('vertical_flip', [False, True]),\n",
" 'rescale': hp.uniform('rescale', 0.0, 0.5),\n",
" 'zoom_range': 0 ,# hp.uniform('zoom_range', 0.0, 0.5),\n",
" 'shear_range': 0, # hp.uniform('shear_range', 0.0, 0.5),\n",
" \n",
" 'max_depth': 50,\n",
" 'batch_size' : 2000*10,\n",
" 'nb_epochs' : 1,\n",
" }\n",
"\n",
"\n",
"def f_dt(params):\n",
" t0 = time.time()\n",
"\n",
" # Get data\n",
" X_train, y_train, metadata_train, X_val, y_val, metadata_val, X_test, y_test, metadata_test = filter_split_data(\n",
" X_raw,\n",
" y_raw,\n",
" metadatas,\n",
" max_cloud_cover=params['max_cloud_cover'], \n",
" timespan_before=params['timespan_before'],\n",
" test_fraction=test_fraction,\n",
" val_fraction=test_fraction,\n",
" random_seed=random_seed,\n",
" balanced_classes=params['balanced_classes'],\n",
" normalized=params['normalized'],\n",
" filter_center_cloudy=params['filter_center_cloudy']\n",
" ) \n",
"\n",
" datagen = ImageDataGenerator(\n",
" # featurewise_center=True,\n",
" # featurewise_std_normalization=True,\n",
" channel_shift_range=params['channel_shift_range'],\n",
" rotation_range=params['rotation_range'],\n",
" width_shift_range=params['width_shift_range'],\n",
" height_shift_range=params['height_shift_range'],\n",
" horizontal_flip=params['horizontal_flip'],\n",
" vertical_flip=params['vertical_flip'],\n",
" rescale=params['rescale'],\n",
" zoom_range=params['zoom_range'],\n",
" shear_range=params['shear_range'],\n",
" dim_ordering='th',\n",
" )\n",
"\n",
" # datagen.fit(X_train)\n",
" \n",
" gen = datagen.flow(X_train, y_train, batch_size=params['batch_size']*params['nb_epochs'])\n",
" X_trainb, y_trainb = next(gen)\n",
" X_train2b = X_trainb.reshape((len(X_trainb),-1))\n",
" X_val2 = X_val.reshape((len(X_val),-1))\n",
" X_test2 = X_test.reshape((len(X_test),-1))\n",
" \n",
" t1=time.time()-t0\n",
"\n",
" # Get model \n",
" model = tree.DecisionTreeRegressor(max_depth=params['max_depth'])\n",
" model.fit(X_train2b, y_trainb) \n",
" y_pred = model.predict(X_val2)\n",
" score = model.score(X_val2, y_val)\n",
"\n",
"\n",
" # score on X_val\n",
" report = parse_classification_report(sklearn.metrics.classification_report(y_val>thresh,y_pred>thresh,target_names=target_names))\n",
" matthews_corrcoef = sklearn.metrics.matthews_corrcoef(y_val>thresh, y_pred>thresh)\n",
" loss=-matthews_corrcoef\n",
" \n",
" # do a dummy score too\n",
" clf = DummyClassifier(strategy='uniform')\n",
" clf.fit(X_train2b,y_trainb)\n",
" y_pred = clf.predict(X_val2)\n",
" score_dummy = clf.score(X_val2, y_val)\n",
" matthews_corrcoef_dummy = sklearn.metrics.matthews_corrcoef(y_val>thresh, y_pred>thresh)\n",
" report_dummy = parse_classification_report(sklearn.metrics.classification_report(y_val>thresh,y_pred>thresh,target_names=target_names))\n",
" \n",
" \n",
"# sys.stdout.flush()\n",
" print(t1,'loss',loss)\n",
" return dict(loss=-loss,\n",
" status=STATUS_OK,\n",
" run_time=t1,\n",
" metrics=dict(\n",
" report=report.to_dict(),\n",
" matthews_corrcoef=matthews_corrcoef,\n",
" ), \n",
" dummy_metrics=dict(\n",
" report_dummy=report_dummy.to_dict(),\n",
" score_dummy=score_dummy,\n",
" matthews_corrcoef_dummy=matthews_corrcoef_dummy,\n",
" ),\n",
" attachments=dict( \n",
"# model=model.__dict__\n",
" )\n",
" )\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-03-26T05:19:36.220Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.595038652420044 loss -0.0386642846727\n",
"0.7030308246612549 loss 0.0932923927765\n",
"1.8368890285491943 loss -0.00368705710051\n",
"1.4079639911651611 loss -0.0166153571975\n",
"1.2733204364776611 loss 0.0494893096183\n",
"1.181145191192627 loss -0.0272881007883\n",
"1.0876176357269287 loss -0.0873704056661\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/isisilon/.virtualenvs/py3syspck/lib/python3.4/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/isisilon/.virtualenvs/py3syspck/lib/python3.4/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars\n",
" mcc = cov_ytyp / np.sqrt(var_yt * var_yp)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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]
}
],
"source": [
"best = fmin(f_dt, space, algo=tpe.suggest, max_evals=1000, trials=trials, verbose=1)\n",
"print ('best: ')\n",
"print (best)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T04:27:39.133867Z",
"start_time": "2017-03-26T12:27:39.130587+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"564"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(trials.trials)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-26T03:02:23.990313Z",
"start_time": "2017-04-26T11:02:23.859646+08:00"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Path' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-4a286114c1ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbson\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson_util\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msave_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../output/hyperopt/{}.json'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exp_key\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 4\u001b[0m \u001b[0msave_path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs_p\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 \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Path' is not defined"
]
}
],
"source": [
"from bson import json_util\n",
"import json\n",
"save_path = Path('../output/hyperopt/{}.json'.format(trials._exp_key))\n",
"save_path.dirname().makedirs_p()\n",
"json.dump(trials.trials,open(save_path,'w'), default=json_util.default)\n",
"save_path"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-26T03:03:01.085510Z",
"start_time": "2017-04-26T11:03:01.079704+08:00"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'save_path' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-18bff864bd4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# load\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrial_data\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_path\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 3\u001b[0m \u001b[0;31m# trial_data =json.load(open(save_path))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'save_path' is not defined"
]
}
],
"source": [
"# load\n",
"trial_data =json.load(open(save_path))\n",
"# trial_data =json.load(open(save_path))\n",
"len(trial_data)"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:13:37.293369Z",
"start_time": "2017-03-26T13:13:36.965767+08:00"
}
},
"outputs": [],
"source": [
"# convert to df\n",
"\n",
"\n",
"def flatten(d, parent_key='', sep='_'):\n",
" \"\"\"Flatten dicts.\"\"\"\n",
" items = []\n",
" for k, v in d.items():\n",
" new_key = parent_key + sep + k if parent_key else k\n",
" if isinstance(v, collections.MutableMapping):\n",
" items.extend(flatten(v, new_key, sep=sep).items())\n",
" else:\n",
" # also flatten lists with one element\n",
" if isinstance(v,list) and len(v)==1:\n",
" v=v[0]\n",
" items.append((new_key, v))\n",
" return dict(items)\n",
"# df = pd.DataFrame([flatten(trial) for trial in trials.trials])\n",
"df = pd.DataFrame([flatten(trial) for trial in trial_data])\n",
"# df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-26T03:02:36.726770Z",
"start_time": "2017-04-26T11:02:36.710408+08:00"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-b47b94b6e60a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'result_metrics_report_f1-score_leak'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# metric='result_metrics_matthews_corrcoef'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m whitelist = [col for col in df.columns if ('misc_vals' in col) or (metric in col)]+[\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m'result_dummy_metrics_report_dummy_f1-score_leak'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m'result_dummy_metrics_report_dummy_support_leak'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"# lets make a table of major results to record\n",
"mean_of=50\n",
"metric='result_metrics_report_f1-score_leak'\n",
"# metric='result_metrics_matthews_corrcoef'\n",
"whitelist = [col for col in df.columns if ('misc_vals' in col) or (metric in col)]+[\n",
" 'result_dummy_metrics_report_dummy_f1-score_leak',\n",
" 'result_dummy_metrics_report_dummy_support_leak',\n",
" \n",
"# 'result_metrics_report_f1-score_leak',\n",
"# 'result_metrics_matthews_corrcoef'\n",
"]\n",
"df50=df.sort_values(metric, ascending=False)[:mean_of][whitelist]\n",
"\n",
"# convert to days\n",
"df50['misc_vals_timespan_before']=df50['misc_vals_timespan_before']/seconds_in_a_day\n",
"\n",
"# columns\n",
"mean=df50.mean()\n",
"std=df50.std()\n",
"corr=df50.corr()[metric]\n",
"units=['bool', 'frac',\n",
" 'frac', 'frac',\n",
" 'bool', 'frac', 'deg',\n",
" 'days', 'frac',\n",
" '','',\n",
" 'int']\n",
"\n",
"# make into df\n",
"filter_results = pd.DataFrame(collections.OrderedDict(\n",
" mean=mean,\n",
" std=std,\n",
" corr=corr))\n",
"filter_results['units']=units\n",
"filter_results=filter_results.round(3)\n",
"\n",
"# print\n",
"print('top {mean_of:} scores, \\n\\n- by metric=\"{metric:}\"\\n- for key=\"{key:}\"\\n- number of trials={n:}\\n'.format(\n",
" mean_of=mean_of,\n",
" key=save_path, \n",
" metric=metric,\n",
" n=len(trials.trials)\n",
"))\n",
"import tabulate\n",
"markdown=tabulate.tabulate(filter_results,tablefmt='pipe',headers=filter_results.columns)\n",
"print(markdown)\n"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T04:30:23.911594Z",
"start_time": "2017-03-26T12:30:23.698324+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fee8fd0aef0>"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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lp/Hx8XzmM5/Jd7/73bS1teXP/uzPMnPmzNx1111pa2vLokWLsn79+nrPCgAANLGa4uOx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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee90e53518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.result_metrics_matthews_corrcoef.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- don't normalise\n",
"- do balance\n",
"- shift off 0.005 ish\n",
"- cloud cover of less than 0.7 (no effect)\n",
"- rotation eange of ~20 deg\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Best filters (for f1 values) for dataset:\n",
"- l7_joined\n",
" - mean of best 50 f1 values\n",
" \n",
" misc_vals_balanced_classes 0.980000\n",
" misc_vals_channel_shift_range 0.083127\n",
" misc_vals_height_shift_range 0.164816\n",
" misc_vals_max_cloud_cover 0.109005\n",
" misc_vals_normalized 0.000000\n",
" misc_vals_rescale 0.329541\n",
" misc_vals_rotation_range 9.903196\n",
" misc_vals_timespan_before 97939.315502 (1.13 days)\n",
" misc_vals_width_shift_range 0.270787\n",
" result_metrics_report_support_avg / total 414.480000\n",
" result_metrics_matthews_corrcoef 0.023856\n",
" result_metrics_report_f1-score_avg / total 0.418200\n",
" result_metrics_report_f1-score_leak 0.678600\n",
" \n",
" - Correlations (how important each is)\n",
" \n",
" misc_vals_balanced_classes 0.623902\n",
" misc_vals_channel_shift_range -0.052222\n",
" misc_vals_max_cloud_cover -0.021244\n",
" misc_vals_normalized -0.196311\n",
" misc_vals_rotation_range -0.113902\n",
" misc_vals_timespan_before 0.084534\n"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:16:53.490256Z",
"start_time": "2017-03-26T13:16:53.485991+08:00"
},
"scrolled": true
},
"outputs": [],
"source": [
"filters = ['misc_vals_','result_metrics_','result_dummy_metrics_report_dummy_f1-score_leak','result_run_time']\n",
"cols = [col for col in df.columns if any([f in col for f in filters])]\n",
"df1 = df[cols]\n",
"# df1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:16:27.420079Z",
"start_time": "2017-03-26T13:16:27.416945+08:00"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-26T05:16:55.421554Z",
"start_time": "2017-03-26T13:16:54.945059+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fee9006d860>"
]
},
"execution_count": 300,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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3yne0Ef2HKZ5xUfEEsDfVK56hMiu/fFRjvqB4BoDVUfnl4ipzo+IZDhrll6Sr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44Qf07t2bmWBTYWEh71360UcfYf369aTxhUJeXh51dXWoqamBRCJBaWkpkzw3btxAVFQUANlh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\nAAAAABB5FK8AAAAAgMj7f9k2jaXN745VAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee90cfd780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# heat map of correlations\n",
"import seaborn as sns\n",
"corr = df1.corr()\n",
"sns.heatmap(corr, \n",
" xticklabels=corr.columns.values,\n",
" yticklabels=corr.columns.values)\n",
"# plt.title('')\n",
"# plt.savefig('/tmp/cryptocorr.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# rotation range etc\n",
"\n",
"**Open the plot below in a new window**\n",
"\n",
"We see rotation range above 25 tends to lose predictive power with 20 being ideal\n",
"\n",
"Cloud cover restrictions seem to do it below 0.6... but I scraped that data to be below 0.3?\n",
"\n",
"Channel shifts>0.01 seems to lose predictive power"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T13:01:29.446749Z",
"start_time": "2017-03-13T21:01:29.441523+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['misc_vals_balanced_classes', 'misc_vals_channel_shift_range',\n",
" 'misc_vals_max_cloud_cover', 'misc_vals_normalized',\n",
" 'misc_vals_rotation_range', 'misc_vals_timespan_before',\n",
" 'misc_vals_filter_center_cloudy',\n",
" 'result_dummy_metrics_matthews_corrcoef_dummy', 'result_loss',\n",
" 'result_metrics_matthews_corrcoef',\n",
" 'result_metrics_report_f1-score_leak',\n",
" 'result_metrics_report_precision_leak',\n",
" 'result_metrics_report_precision_no leak',\n",
" 'result_metrics_report_recall_avg / total',\n",
" 'result_metrics_report_recall_leak',\n",
" 'result_metrics_report_recall_no leak',\n",
" 'result_metrics_report_support_avg / total', 'result_run_time'],\n",
" dtype='object')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.columns"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T13:15:41.051905Z",
"start_time": "2017-03-13T21:15:31.640899+08:00"
}
},
"outputs": [
{
"ename": "ValueError",
"evalue": "max must be larger than min in range parameter.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-37-2278525d9e86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mdf5\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'precision_leak'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'result_metrics_report_precision_leak'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mdf5\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'misc_vals_timespan_before'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc_vals_timespan_before\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mseconds_in_a_day\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/isisilon/.virtualenvs/py3syspck/lib/python3.4/site-packages/seaborn/linearmodels.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, size, aspect, dropna, plot_kws, diag_kws, grid_kws)\u001b[0m\n\u001b[1;32m 1607\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare_grid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1608\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1609\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1610\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1611\u001b[0m \u001b[0mdiag_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"legend\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/isisilon/.virtualenvs/py3syspck/lib/python3.4/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 1346\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1347\u001b[0m func(vals, color=self.palette, histtype=\"barstacked\",\n\u001b[0;32m-> 1348\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 1349\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_k\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhue_names\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/python3.4/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 2956\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2957\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2958\u001b[0;31m stacked=stacked, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 2959\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2960\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1810\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1811\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1812\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1813\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1814\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.4/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m 6008\u001b[0m \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6009\u001b[0m \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6010\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6011\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6012\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/isisilon/.virtualenvs/py3syspck/lib/python3.4/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmn\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 665\u001b[0m raise ValueError(\n\u001b[0;32m--> 666\u001b[0;31m 'max must be larger than min in range parameter.')\n\u001b[0m\u001b[1;32m 667\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmx\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 668\u001b[0m raise ValueError(\n",
"\u001b[0;31mValueError\u001b[0m: max must be larger than min in range parameter."
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f542852dd30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f54362a0ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# this show the relationship between predictive power and days before\n",
"# so to get a good correlation we want \n",
"sns.set()\n",
"plt.figure(figsize=(15,15))\n",
"df5=df[[\n",
" 'result_metrics_matthews_corrcoef',\n",
" 'misc_vals_channel_shift_range',\n",
" 'misc_vals_max_cloud_cover', 'misc_vals_rotation_range'\n",
"]].copy()\n",
"df5['n']=df['result_metrics_report_support_avg / total']\n",
"df5['f-score']=df1['result_metrics_report_f1-score_leak']\n",
"df5['precision*recall_leak']=df['result_metrics_report_recall_leak']*df['result_metrics_report_precision_leak']\n",
"df5['precision_leak']=df['result_metrics_report_precision_leak']\n",
"df5['misc_vals_timespan_before']=df.misc_vals_timespan_before/seconds_in_a_day\n",
"sns.pairplot(df5,size=6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-11T02:40:51.258612Z",
"start_time": "2017-03-11T10:40:51.255108+08:00"
}
},
"source": [
"# How many days before?\n",
"\n",
"This show how many days before we can go before we lose predictive power (either good or bad)\n",
"\n",
"If you look at the top right graph, we lose predictive power at greater than 5 days. Before that its\n",
"exponential increase but I also get a drop of in data. \n",
"\n",
"So: 2 days looks like the sweet spot."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-11T02:39:06.095251Z",
"start_time": "2017-03-11T10:39:06.091162+08:00"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T13:01:38.829306Z",
"start_time": "2017-03-13T13:01:30.517Z"
}
},
"outputs": [],
"source": [
"# this show the relationship between predictive power and days before\n",
"# so to get a good correlation we want \n",
"sns.set()\n",
"plt.figure(figsize=(15,15))\n",
"df5=df[[\n",
" 'result_metrics_matthews_corrcoef'\n",
"]].copy()\n",
"df5['n']=df['result_metrics_report_support_avg / total']\n",
"df5['misc_vals_timespan_before']=df.misc_vals_timespan_before/seconds_in_a_day\n",
"sns.pairplot(df5,size=6)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"start_time": "2017-03-12T05:47:14.443Z"
},
"code_folding": [],
"collapsed": true
},
"source": [
"# Hyperopt for randomforest"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-12T06:07:40.971308Z",
"start_time": "2017-03-12T14:07:40.941865+08:00"
},
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T13:01:52.178324Z",
"start_time": "2017-03-13T21:01:52.057741+08:00"
}
},
"outputs": [],
"source": [
"\"\"\"\n",
"This experiment will look at the best random forest params\n",
"\"\"\"\n",
"\n",
"from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
"from sklearn.metrics import roc_auc_score\n",
"import sys\n",
"from sklearn.dummy import DummyClassifier\n",
"import sklearn.ensemble\n",
"import time\n",
"seconds_in_a_day = 60*60*24\n",
"\n",
"trials2 = Trials(exp_key='data_filters_v3_rf_ATX')\n",
"# trials = MongoTrials('mongo://localhost:27017/hyperopt_leaks2/jobs', exp_key='data_filters_v2')\n",
"\n",
"def hp_int(label,min,max):\n",
" return hp.choice(label, np.arange(min, max, dtype=int)) \n",
"\n",
"\n",
"space = {\n",
"\n",
"\n",
"\n",
" # rf\n",
" 'n_estimators': hp_int('n_estimators',2,300),\n",
" 'criterion': hp.choice('criterion',['gini','entropy']),\n",
" 'max_features': hp_int('max_features',2,300),\n",
" 'max_depth': hp_int('max_depth',2,300),\n",
" 'min_samples_split': hp_int('min_samples_split',1,10),\n",
" 'min_samples_leaf': hp_int('min_samples_leaf',1,10),\n",
" 'bootstrap':hp.choice('bootstrap',[False,True]),\n",
" \n",
" \n",
" 'min_weight_fraction_leaf': 0, # hp.float('min_weight_fraction_leaf',2,10),\n",
" 'max_leaf_nodes': None, # hp.int('max_leaf_nodes',2,300),\n",
" 'min_impurity_split': 1e-7, #hp.float('min_impurity_split',1e-12,1e-3),\n",
" \n",
" 'oob_score': False, # hp.choice('oob_score',[False,True]),\n",
" 'n_jobs':4,\n",
" 'random_state':0,\n",
" 'verbose':0,\n",
" 'warm_start':False,\n",
" 'class_weight':None,\n",
" \n",
" \n",
" # data filter\n",
" 'max_cloud_cover': 0.3,\n",
" 'timespan_before': seconds_in_a_day*3,\n",
" 'balanced_classes':True,\n",
" 'normalized': False,\n",
" 'filter_center_cloudy': True,\n",
" \n",
" # std is 5% so max should prob be ~5%\n",
" 'channel_shift_range': 0.07,\n",
" 'rotation_range': 25,\n",
" \n",
" 'max_depth': 50,\n",
" 'batch_size' : 2000*10,\n",
" 'nb_epochs' : 1,\n",
" }\n",
"\n",
"\n",
"def f_rf(params):\n",
" t0 = time.time()\n",
"\n",
" # Get data\n",
" X_train, y_train, metadata_train, X_val, y_val, metadata_val, X_test, y_test, metadata_test = filter_split_data(\n",
" X_raw,\n",
" y_raw,\n",
" metadatas,\n",
" max_cloud_cover=params['max_cloud_cover'], \n",
" timespan_before=params['timespan_before'],\n",
" test_fraction=test_fraction, \n",
" random_seed=random_seed,\n",
" balanced_classes=params['balanced_classes'],\n",
" normalized=params['normalized'],\n",
" filter_center_cloudy=params['filter_center_cloudy']\n",
" ) \n",
"\n",
" datagen = ImageDataGenerator(\n",
" # featurewise_center=True,\n",
" # featurewise_std_normalization=True,\n",
" channel_shift_range=params['channel_shift_range'],\n",
" rotation_range=params['rotation_range'],\n",
" # width_shift_range=0.05,\n",
" # height_shift_range=0.05,\n",
" horizontal_flip=True,\n",
" vertical_flip=True,\n",
" dim_ordering='th',\n",
" # rescale=0.02,\n",
" # zoom_range=0.05,\n",
" # shear_range=0.05,\n",
" )\n",
"\n",
" # datagen.fit(X_train)\n",
" \n",
" gen = datagen.flow(X_train, y_train, batch_size=params['batch_size']*params['nb_epochs'])\n",
" X_trainb, y_trainb = next(gen)\n",
" X_train2b = X_trainb.reshape((-1,14*25*25))\n",
" X_val2 = X_val.reshape((-1,14*25*25))\n",
" X_test2 = X_test.reshape((-1,14*25*25))\n",
" \n",
" t1=time.time()-t0\n",
"\n",
" # Get model \n",
" model = sklearn.ensemble.RandomForestClassifier(\n",
" n_estimators=params['n_estimators'],\n",
" criterion=params['criterion'],\n",
" max_features=params['max_features'],\n",
" max_depth=params['max_depth'],\n",
" min_samples_split=params['min_samples_split'], \n",
" min_samples_leaf=params['min_samples_leaf'],\n",
" max_leaf_nodes=params['max_leaf_nodes'],\n",
" min_impurity_split=params['min_impurity_split'],\n",
" bootstrap=params['bootstrap'],\n",
" oob_score=params['oob_score'],\n",
" n_jobs=params['n_jobs'],\n",
"# random_state=params['random_state'],\n",
" verbose=params['verbose'],\n",
" warm_start=params['warm_start'],\n",
" class_weight=params['class_weight'],\n",
" \n",
" )\n",
" model.fit(X_train2b, y_trainb) \n",
" y_pred = model.predict(X_val2)\n",
" score = model.score(X_val2, y_val)\n",
"\n",
" # score on X_val\n",
" report = parse_classification_report(sklearn.metrics.classification_report(y_val>thresh,y_pred>thresh,target_names=target_names))\n",
" matthews_corrcoef = sklearn.metrics.matthews_corrcoef(y_val>thresh, y_pred>thresh)\n",
" loss=-matthews_corrcoef\n",
" \n",
"# sys.stdout.flush()\n",
" print(t1,'loss',loss)\n",
" return dict(loss=loss,\n",
" status=STATUS_OK,\n",
" run_time=t1,\n",
" metrics=dict(\n",
" report=report.to_dict(),\n",
" matthews_corrcoef=matthews_corrcoef,\n",
" ), \n",
" attachments=dict( \n",
"# model=model.__dict__\n",
" )\n",
" )\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T17:45:50.034094Z",
"start_time": "2017-03-13T21:21:34.385522+08:00"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"1.9284241199493408 loss -0.0419666607379\n",
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"2.224534749984741 loss -0.0214593655723\n",
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"1.9693429470062256 loss -0.11771247078\n",
"1.9181501865386963 loss -0.0785766360178\n",
"1.8544762134552002 loss -0.107263522845\n",
"1.845444917678833 loss -0.0914612730523\n",
"2.002821683883667 loss -0.105832695994\n",
"2.111935615539551 loss -0.056105130691\n",
"2.346569538116455 loss -0.0120833593845\n",
"1.8831851482391357 loss -0.0335388080564\n",
"1.934401512145996 loss -0.0528129861336\n",
"1.8980915546417236 loss -0.128559795226\n",
"2.0068023204803467 loss -0.0626375224311\n",
"1.9175426959991455 loss -0.085719376627\n",
"1.9139907360076904 loss -0.0615924671148\n",
"2.01072096824646 loss -0.0349325588233\n",
"1.9846551418304443 loss -0.055498725006\n",
"1.9855327606201172 loss -0.127974151297\n",
"1.9402661323547363 loss -0.176972975147\n",
"2.038374423980713 loss -0.118146728826\n",
"1.8805246353149414 loss -0.0404222737637\n",
"2.0239107608795166 loss -0.184058955381\n",
"1.890681505203247 loss -0.0772189157487\n",
"1.9313075542449951 loss -0.104937885349\n",
"1.881563663482666 loss -0.0774641309732\n",
"2.150602102279663 loss -0.0873580309709\n",
"1.9293596744537354 loss -0.102219590445\n",
"2.086696147918701 loss -0.0550660792952\n",
"1.8965351581573486 loss -0.122555772184\n",
"1.883742332458496 loss -0.0220544935899\n",
"2.2133936882019043 loss -0.110992674275\n",
"1.8922266960144043 loss -0.0241791313382\n",
"2.0148770809173584 loss -0.103871896467\n",
"1.9736952781677246 loss -0.100213959866\n",
"1.9761126041412354 loss -0.0439573019772\n",
"1.9419691562652588 loss -0.0940340593793\n",
"2.2537240982055664 loss -0.0439573019772\n",
"best: \n",
"{'min_samples_split': 3, 'max_features': 52, 'criterion': 1, 'bootstrap': 1, 'n_estimators': 274, 'min_samples_leaf': 6}\n"
]
}
],
"source": [
"best = fmin(f_rf, space, algo=tpe.suggest, max_evals=1000, trials=trials2, verbose=1)\n",
"print ('best: ')\n",
"print (best)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-03-25T03:34:56.906Z"
}
},
"outputs": [],
"source": [
"trials._exp_key=str(data_path.basename())\n",
"len(trials.trials), len(trials2.trials)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-03-25T03:34:04.157Z"
}
},
"outputs": [],
"source": [
"from bson import json_util\n",
"import json\n",
"save_path = Path('../output/hyperopt/{}.json'.format(trials._exp_key))\n",
"save_path.dirname().makedirs_p()\n",
"json.dump(trials.trials,open(save_path,'w'), default=json_util.default)\n",
"save_path"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T20:49:01.580712Z",
"start_time": "2017-03-14T04:49:01.443994+08:00"
}
},
"outputs": [],
"source": [
"import collections\n",
"\n",
"def flatten(d, parent_key='', sep='_'):\n",
" \"\"\"Flatten dicts.\"\"\"\n",
" items = []\n",
" for k, v in d.items():\n",
" new_key = parent_key + sep + k if parent_key else k\n",
" if isinstance(v, collections.MutableMapping):\n",
" items.extend(flatten(v, new_key, sep=sep).items())\n",
" else:\n",
" # also flatten lists with one element\n",
" if isinstance(v,list) and len(v)==1:\n",
" v=v[0]\n",
" items.append((new_key, v))\n",
" return dict(items)\n",
"df = pd.DataFrame([flatten(trial) for trial in trials2.trials])\n",
"# df = pd.DataFrame([flatten(trial) for trial in trial_data])\n",
"# df"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T20:49:02.033997Z",
"start_time": "2017-03-14T04:49:02.031426+08:00"
}
},
"outputs": [],
"source": [
"# df.sort_values('result_metrics_matthews_corrcoef')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T20:49:02.317316Z",
"start_time": "2017-03-14T04:49:02.307996+08:00"
}
},
"outputs": [],
"source": [
"df2=df[[\n",
"# 'book_time',\n",
"# 'exp_key',\n",
"# 'misc_cmd',\n",
"# 'misc_idxs_bootstrap',\n",
"# 'misc_idxs_criterion',\n",
"# 'misc_idxs_max_features',\n",
"# 'misc_idxs_min_samples_leaf',\n",
"# 'misc_idxs_min_samples_split',\n",
"# 'misc_idxs_n_estimators',\n",
"# 'misc_tid',\n",
" 'misc_vals_bootstrap',\n",
" 'misc_vals_criterion',\n",
" 'misc_vals_max_features',\n",
" 'misc_vals_min_samples_leaf',\n",
" 'misc_vals_min_samples_split',\n",
" 'misc_vals_n_estimators',\n",
"# 'misc_workdir',\n",
"# 'owner',\n",
"# 'refresh_time',\n",
"# 'result_loss',\n",
" 'result_metrics_matthews_corrcoef',\n",
"# 'result_metrics_report_f1-score_avg / total',\n",
" 'result_metrics_report_f1-score_leak',\n",
"# 'result_metrics_report_f1-score_no leak',\n",
"# 'result_metrics_report_precision_avg / total',\n",
" 'result_metrics_report_precision_leak',\n",
"# 'result_metrics_report_precision_no leak',\n",
"# 'result_metrics_report_recall_avg / total',\n",
" 'result_metrics_report_recall_leak',\n",
"# 'result_metrics_report_recall_no leak',\n",
"# 'result_metrics_report_support_avg / total',\n",
"# 'result_metrics_report_support_leak',\n",
"# 'result_metrics_report_support_no leak',\n",
"# 'result_run_time',\n",
"# 'result_status',\n",
"# 'spec',\n",
"# 'state',\n",
"# 'tid',\n",
"# 'version'\n",
" ]].sort_values('result_metrics_matthews_corrcoef',ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- critenrion = 'entropy'\n",
"- max_features ~ 100-260\n",
"- min_samples_leaf=5\n",
"- min_samples_split=5?\n",
"- n_estimators=~160\n",
"- boostrap, yues?\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T20:49:03.599438Z",
"start_time": "2017-03-14T04:49:03.562607+08:00"
}
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>misc_vals_bootstrap</th>\n",
" <th>misc_vals_criterion</th>\n",
" <th>misc_vals_max_features</th>\n",
" <th>misc_vals_min_samples_leaf</th>\n",
" <th>misc_vals_min_samples_split</th>\n",
" <th>misc_vals_n_estimators</th>\n",
" <th>result_metrics_matthews_corrcoef</th>\n",
" <th>result_metrics_report_f1-score_leak</th>\n",
" <th>result_metrics_report_precision_leak</th>\n",
" <th>result_metrics_report_recall_leak</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>981</th>\n",
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" <td>6</td>\n",
" <td>208</td>\n",
" <td>0.176973</td>\n",
" <td>0.59</td>\n",
" <td>0.62</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>6</td>\n",
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" <td>0.63</td>\n",
" <td>0.54</td>\n",
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" <td>1</td>\n",
" <td>248</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>99</td>\n",
" <td>0.175259</td>\n",
" <td>0.60</td>\n",
" <td>0.62</td>\n",
" <td>0.58</td>\n",
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" <th>639</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>107</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>0.175259</td>\n",
" <td>0.60</td>\n",
" <td>0.62</td>\n",
" <td>0.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>589</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>83</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>286</td>\n",
" <td>0.171789</td>\n",
" <td>0.61</td>\n",
" <td>0.61</td>\n",
" <td>0.61</td>\n",
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" <tr>\n",
" <th>647</th>\n",
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" <td>1</td>\n",
" <td>54</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>294</td>\n",
" <td>0.162767</td>\n",
" <td>0.57</td>\n",
" <td>0.62</td>\n",
" <td>0.53</td>\n",
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" <td>274</td>\n",
" <td>0.162663</td>\n",
" <td>0.59</td>\n",
" <td>0.61</td>\n",
" <td>0.56</td>\n",
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" <td>1</td>\n",
" <td>238</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>253</td>\n",
" <td>0.160757</td>\n",
" <td>0.58</td>\n",
" <td>0.61</td>\n",
" <td>0.54</td>\n",
" </tr>\n",
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" <th>851</th>\n",
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" <td>136</td>\n",
" <td>0.160444</td>\n",
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" <td>0.61</td>\n",
" <td>0.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>620</th>\n",
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" <td>152</td>\n",
" <td>0.160117</td>\n",
" <td>0.58</td>\n",
" <td>0.61</td>\n",
" <td>0.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>56</td>\n",
" <td>0.159126</td>\n",
" <td>0.56</td>\n",
" <td>0.62</td>\n",
" <td>0.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>190</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>174</td>\n",
" <td>0.157980</td>\n",
" <td>0.60</td>\n",
" <td>0.60</td>\n",
" <td>0.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>653</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>107</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>79</td>\n",
" <td>0.157733</td>\n",
" <td>0.57</td>\n",
" <td>0.62</td>\n",
" <td>0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>600</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>37</td>\n",
" <td>0.155506</td>\n",
" <td>0.56</td>\n",
" <td>0.62</td>\n",
" <td>0.51</td>\n",
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" <tr>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>131</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>102</td>\n",
" <td>0.154997</td>\n",
" <td>0.60</td>\n",
" <td>0.60</td>\n",
" <td>0.59</td>\n",
" </tr>\n",
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" <th>929</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>145</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>0.154997</td>\n",
" <td>0.60</td>\n",
" <td>0.60</td>\n",
" <td>0.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>801</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>65</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>105</td>\n",
" <td>0.153534</td>\n",
" <td>0.60</td>\n",
" <td>0.60</td>\n",
" <td>0.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>420</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>141</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>167</td>\n",
" <td>0.153320</td>\n",
" <td>0.58</td>\n",
" <td>0.61</td>\n",
" <td>0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>109</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>196</td>\n",
" <td>0.153320</td>\n",
" <td>0.58</td>\n",
" <td>0.61</td>\n",
" <td>0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>473</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>22</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>105</td>\n",
" <td>0.152185</td>\n",
" <td>0.59</td>\n",
" <td>0.61</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>99</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>58</td>\n",
" <td>0.152185</td>\n",
" <td>0.59</td>\n",
" <td>0.61</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>75</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>76</td>\n",
" <td>0.151765</td>\n",
" <td>0.61</td>\n",
" <td>0.60</td>\n",
" <td>0.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>785</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>69</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>212</td>\n",
" <td>0.150582</td>\n",
" <td>0.59</td>\n",
" <td>0.60</td>\n",
" <td>0.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>257</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>166</td>\n",
" <td>0.008231</td>\n",
" <td>0.51</td>\n",
" <td>0.53</td>\n",
" <td>0.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>319</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>107</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>33</td>\n",
" <td>0.008231</td>\n",
" <td>0.51</td>\n",
" <td>0.53</td>\n",
" <td>0.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>771</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>229</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>270</td>\n",
" <td>0.007686</td>\n",
" <td>0.51</td>\n",
" <td>0.53</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>945</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>108</td>\n",
" <td>0.007673</td>\n",
" <td>0.49</td>\n",
" <td>0.53</td>\n",
" <td>0.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>749</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>245</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>165</td>\n",
" <td>0.007141</td>\n",
" <td>0.52</td>\n",
" <td>0.53</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>842</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>90</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>99</td>\n",
" <td>0.006124</td>\n",
" <td>0.54</td>\n",
" <td>0.53</td>\n",
" <td>0.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>901</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>125</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>260</td>\n",
" <td>0.006021</td>\n",
" <td>0.50</td>\n",
" <td>0.53</td>\n",
" <td>0.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>772</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>254</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>0.004967</td>\n",
" <td>0.53</td>\n",
" <td>0.53</td>\n",
" <td>0.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>694</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>157</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>150</td>\n",
" <td>0.004379</td>\n",
" <td>0.51</td>\n",
" <td>0.53</td>\n",
" <td>0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>203</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>58</td>\n",
" <td>0.004379</td>\n",
" <td>0.51</td>\n",
" <td>0.53</td>\n",
" <td>0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>481</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>260</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>267</td>\n",
" <td>0.002794</td>\n",
" <td>0.53</td>\n",
" <td>0.53</td>\n",
" <td>0.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>248</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>37</td>\n",
" <td>0.001655</td>\n",
" <td>0.52</td>\n",
" <td>0.53</td>\n",
" <td>0.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>795</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>152</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>-0.000523</td>\n",
" <td>0.53</td>\n",
" <td>0.53</td>\n",
" <td>0.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>471</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>255</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>270</td>\n",
" <td>-0.002743</td>\n",
" <td>0.51</td>\n",
" <td>0.53</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>535</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>151</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>17</td>\n",
" <td>-0.004967</td>\n",
" <td>0.50</td>\n",
" <td>0.53</td>\n",
" <td>0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>65</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>38</td>\n",
" <td>-0.006665</td>\n",
" <td>0.49</td>\n",
" <td>0.53</td>\n",
" <td>0.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>206</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>19</td>\n",
" <td>-0.007747</td>\n",
" <td>0.49</td>\n",
" <td>0.53</td>\n",
" <td>0.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>68</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>119</td>\n",
" <td>-0.008828</td>\n",
" <td>0.50</td>\n",
" <td>0.52</td>\n",
" <td>0.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>258</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>207</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>-0.011529</td>\n",
" <td>0.53</td>\n",
" <td>0.52</td>\n",
" <td>0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>861</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>193</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>30</td>\n",
" <td>-0.011594</td>\n",
" <td>0.55</td>\n",
" <td>0.52</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>199</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>23</td>\n",
" <td>-0.011621</td>\n",
" <td>0.49</td>\n",
" <td>0.52</td>\n",
" <td>0.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>-0.018673</td>\n",
" <td>0.52</td>\n",
" <td>0.52</td>\n",
" <td>0.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>84</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>-0.019794</td>\n",
" <td>0.50</td>\n",
" <td>0.52</td>\n",
" <td>0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>220</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>109</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>130</td>\n",
" <td>-0.022054</td>\n",
" <td>0.49</td>\n",
" <td>0.52</td>\n",
" <td>0.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>266</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>204</td>\n",
" <td>-0.024735</td>\n",
" <td>0.50</td>\n",
" <td>0.52</td>\n",
" <td>0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>58</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>-0.025932</td>\n",
" <td>0.39</td>\n",
" <td>0.51</td>\n",
" <td>0.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>864</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>228</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.033539</td>\n",
" <td>0.49</td>\n",
" <td>0.51</td>\n",
" <td>0.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>796</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>160</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>-0.040652</td>\n",
" <td>0.50</td>\n",
" <td>0.51</td>\n",
" <td>0.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>67</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>-0.041648</td>\n",
" <td>0.46</td>\n",
" <td>0.51</td>\n",
" <td>0.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>690</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>279</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>61</td>\n",
" <td>-0.048357</td>\n",
" <td>0.49</td>\n",
" <td>0.50</td>\n",
" <td>0.47</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" misc_vals_bootstrap misc_vals_criterion misc_vals_max_features \\\n",
"482 1 1 52 \n",
"708 0 1 153 \n",
"984 0 1 170 \n",
"230 0 1 140 \n",
"635 1 1 89 \n",
"1 0 1 22 \n",
"369 0 1 142 \n",
"981 1 1 113 \n",
"783 1 1 45 \n",
"318 1 1 248 \n",
"639 0 1 107 \n",
"589 1 1 83 \n",
"647 0 1 54 \n",
"730 0 1 33 \n",
"146 1 1 238 \n",
"851 0 1 49 \n",
"620 0 1 4 \n",
"876 0 1 7 \n",
"409 1 1 190 \n",
"653 0 1 107 \n",
"600 1 1 3 \n",
"595 1 1 131 \n",
"929 1 0 145 \n",
"801 1 1 65 \n",
"420 1 1 141 \n",
"867 1 1 109 \n",
"473 0 1 22 \n",
"45 1 1 99 \n",
"386 0 1 75 \n",
"785 1 1 69 \n",
".. ... ... ... \n",
"127 1 1 257 \n",
"319 1 1 107 \n",
"771 1 1 229 \n",
"945 1 1 20 \n",
"749 1 0 245 \n",
"842 0 1 90 \n",
"901 1 1 125 \n",
"772 0 1 254 \n",
"694 0 0 157 \n",
"64 1 1 203 \n",
"481 0 1 260 \n",
"324 0 0 248 \n",
"795 1 1 152 \n",
"471 0 1 255 \n",
"535 0 1 151 \n",
"182 0 1 65 \n",
"562 1 1 206 \n",
"499 0 1 68 \n",
"258 1 1 207 \n",
"861 1 1 193 \n",
"555 0 1 199 \n",
"368 1 1 200 \n",
"869 1 0 84 \n",
"220 1 1 109 \n",
"171 0 1 266 \n",
"385 0 1 58 \n",
"864 0 1 228 \n",
"796 1 1 160 \n",
"353 1 1 67 \n",
"690 0 1 279 \n",
"\n",
" misc_vals_min_samples_leaf misc_vals_min_samples_split \\\n",
"482 6 3 \n",
"708 7 6 \n",
"984 5 6 \n",
"230 6 6 \n",
"635 7 6 \n",
"1 6 1 \n",
"369 5 0 \n",
"981 7 6 \n",
"783 6 7 \n",
"318 7 3 \n",
"639 7 2 \n",
"589 6 3 \n",
"647 7 2 \n",
"730 7 6 \n",
"146 2 6 \n",
"851 5 3 \n",
"620 6 6 \n",
"876 6 6 \n",
"409 6 3 \n",
"653 7 2 \n",
"600 2 6 \n",
"595 6 3 \n",
"929 6 6 \n",
"801 6 7 \n",
"420 6 3 \n",
"867 5 3 \n",
"473 6 3 \n",
"45 7 1 \n",
"386 3 4 \n",
"785 6 7 \n",
".. ... ... \n",
"127 8 1 \n",
"319 7 3 \n",
"771 4 4 \n",
"945 6 6 \n",
"749 1 1 \n",
"842 4 4 \n",
"901 1 6 \n",
"772 6 0 \n",
"694 7 6 \n",
"64 0 8 \n",
"481 1 4 \n",
"324 7 3 \n",
"795 6 7 \n",
"471 2 0 \n",
"535 5 6 \n",
"182 3 5 \n",
"562 7 3 \n",
"499 6 5 \n",
"258 6 6 \n",
"861 7 7 \n",
"555 1 6 \n",
"368 2 1 \n",
"869 1 6 \n",
"220 1 6 \n",
"171 4 3 \n",
"385 1 2 \n",
"864 6 1 \n",
"796 6 7 \n",
"353 4 8 \n",
"690 3 6 \n",
"\n",
" misc_vals_n_estimators result_metrics_matthews_corrcoef \\\n",
"482 274 0.184599 \n",
"708 96 0.184059 \n",
"984 74 0.184059 \n",
"230 132 0.182557 \n",
"635 127 0.180301 \n",
"1 208 0.179479 \n",
"369 207 0.177196 \n",
"981 208 0.176973 \n",
"783 297 0.176490 \n",
"318 99 0.175259 \n",
"639 36 0.175259 \n",
"589 286 0.171789 \n",
"647 294 0.162767 \n",
"730 274 0.162663 \n",
"146 253 0.160757 \n",
"851 136 0.160444 \n",
"620 152 0.160117 \n",
"876 56 0.159126 \n",
"409 174 0.157980 \n",
"653 79 0.157733 \n",
"600 37 0.155506 \n",
"595 102 0.154997 \n",
"929 57 0.154997 \n",
"801 105 0.153534 \n",
"420 167 0.153320 \n",
"867 196 0.153320 \n",
"473 105 0.152185 \n",
"45 58 0.152185 \n",
"386 76 0.151765 \n",
"785 212 0.150582 \n",
".. ... ... \n",
"127 166 0.008231 \n",
"319 33 0.008231 \n",
"771 270 0.007686 \n",
"945 108 0.007673 \n",
"749 165 0.007141 \n",
"842 99 0.006124 \n",
"901 260 0.006021 \n",
"772 22 0.004967 \n",
"694 150 0.004379 \n",
"64 58 0.004379 \n",
"481 267 0.002794 \n",
"324 37 0.001655 \n",
"795 6 -0.000523 \n",
"471 270 -0.002743 \n",
"535 17 -0.004967 \n",
"182 38 -0.006665 \n",
"562 19 -0.007747 \n",
"499 119 -0.008828 \n",
"258 18 -0.011529 \n",
"861 30 -0.011594 \n",
"555 23 -0.011621 \n",
"368 36 -0.018673 \n",
"869 70 -0.019794 \n",
"220 130 -0.022054 \n",
"171 204 -0.024735 \n",
"385 0 -0.025932 \n",
"864 3 -0.033539 \n",
"796 11 -0.040652 \n",
"353 4 -0.041648 \n",
"690 61 -0.048357 \n",
"\n",
" result_metrics_report_f1-score_leak \\\n",
"482 0.60 \n",
"708 0.60 \n",
"984 0.60 \n",
"230 0.59 \n",
"635 0.62 \n",
"1 0.59 \n",
"369 0.58 \n",
"981 0.59 \n",
"783 0.58 \n",
"318 0.60 \n",
"639 0.60 \n",
"589 0.61 \n",
"647 0.57 \n",
"730 0.59 \n",
"146 0.58 \n",
"851 0.59 \n",
"620 0.58 \n",
"876 0.56 \n",
"409 0.60 \n",
"653 0.57 \n",
"600 0.56 \n",
"595 0.60 \n",
"929 0.60 \n",
"801 0.60 \n",
"420 0.58 \n",
"867 0.58 \n",
"473 0.59 \n",
"45 0.59 \n",
"386 0.61 \n",
"785 0.59 \n",
".. ... \n",
"127 0.51 \n",
"319 0.51 \n",
"771 0.51 \n",
"945 0.49 \n",
"749 0.52 \n",
"842 0.54 \n",
"901 0.50 \n",
"772 0.53 \n",
"694 0.51 \n",
"64 0.51 \n",
"481 0.53 \n",
"324 0.52 \n",
"795 0.53 \n",
"471 0.51 \n",
"535 0.50 \n",
"182 0.49 \n",
"562 0.49 \n",
"499 0.50 \n",
"258 0.53 \n",
"861 0.55 \n",
"555 0.49 \n",
"368 0.52 \n",
"869 0.50 \n",
"220 0.49 \n",
"171 0.50 \n",
"385 0.39 \n",
"864 0.49 \n",
"796 0.50 \n",
"353 0.46 \n",
"690 0.49 \n",
"\n",
" result_metrics_report_precision_leak result_metrics_report_recall_leak \n",
"482 0.62 0.59 \n",
"708 0.62 0.59 \n",
"984 0.62 0.59 \n",
"230 0.62 0.56 \n",
"635 0.61 0.63 \n",
"1 0.62 0.55 \n",
"369 0.63 0.53 \n",
"981 0.62 0.57 \n",
"783 0.63 0.54 \n",
"318 0.62 0.58 \n",
"639 0.62 0.58 \n",
"589 0.61 0.61 \n",
"647 0.62 0.53 \n",
"730 0.61 0.56 \n",
"146 0.61 0.54 \n",
"851 0.61 0.58 \n",
"620 0.61 0.55 \n",
"876 0.62 0.52 \n",
"409 0.60 0.60 \n",
"653 0.62 0.53 \n",
"600 0.62 0.51 \n",
"595 0.60 0.59 \n",
"929 0.60 0.59 \n",
"801 0.60 0.60 \n",
"420 0.61 0.56 \n",
"867 0.61 0.56 \n",
"473 0.61 0.57 \n",
"45 0.61 0.57 \n",
"386 0.60 0.62 \n",
"785 0.60 0.58 \n",
".. ... ... \n",
"127 0.53 0.49 \n",
"319 0.53 0.49 \n",
"771 0.53 0.50 \n",
"945 0.53 0.46 \n",
"749 0.53 0.50 \n",
"842 0.53 0.55 \n",
"901 0.53 0.47 \n",
"772 0.53 0.52 \n",
"694 0.53 0.48 \n",
"64 0.53 0.48 \n",
"481 0.53 0.54 \n",
"324 0.53 0.51 \n",
"795 0.53 0.52 \n",
"471 0.53 0.50 \n",
"535 0.53 0.48 \n",
"182 0.53 0.45 \n",
"562 0.53 0.46 \n",
"499 0.52 0.47 \n",
"258 0.52 0.53 \n",
"861 0.52 0.57 \n",
"555 0.52 0.45 \n",
"368 0.52 0.51 \n",
"869 0.52 0.48 \n",
"220 0.52 0.46 \n",
"171 0.52 0.48 \n",
"385 0.51 0.32 \n",
"864 0.51 0.47 \n",
"796 0.51 0.49 \n",
"353 0.51 0.41 \n",
"690 0.50 0.47 \n",
"\n",
"[1000 rows x 10 columns]"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"ExecuteTime": {
"end_time": "2017-03-13T20:49:30.438434Z",
"start_time": "2017-03-14T04:49:30.432507+08:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"misc_vals_bootstrap 0.540000\n",
"misc_vals_criterion 0.980000\n",
"misc_vals_max_features 102.040000\n",
"misc_vals_min_samples_leaf 5.840000\n",
"misc_vals_min_samples_split 4.380000\n",
"misc_vals_n_estimators 160.840000\n",
"result_metrics_matthews_corrcoef 0.157871\n",
"result_metrics_report_f1-score_leak 0.584600\n",
"result_metrics_report_precision_leak 0.609400\n",
"result_metrics_report_recall_leak 0.564200\n",
"dtype: float64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2[:50].mean()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-04-26T03:03:28.268144Z",
"start_time": "2017-04-26T11:03:28.263313+08:00"
}
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-246e620b3f38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m482\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"df.iloc[482]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (with sys packages)",
"language": "python",
"name": "py3syspck"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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