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submission_splitted_category.py
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submission_splitted_category.py
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from data_io import DataIO
from sklearn.ensemble import ExtraTreesRegressor
#from sklearn.cross_validation import cross_val_score
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
dio = DataIO("Settings.json")
submission = False
n_trees = 10
min_samples_split = 2
if submission:
type_n = "train_full"
type_v = "valid_full"
else:
type_n = "train"
type_v = "valid"
vectorizer = CountVectorizer(
max_features=200,
)
short_id = "count_200f"
tfidf_columns = ["Title", "FullDescription", "LocationRaw"]
#dio.make_counts(vectorizer, short_id, tfidf_columns, type_n, type_v)
columns = ["Category", "ContractTime", "ContractType"]
le_features = dio.get_le_features(columns, "train_full")
extra_features = dio.get_features(columns, type_n, le_features)
extra_valid_features = dio.get_features(columns, type_v, le_features)
split_name = "Category"
#split_name = "ContractTime"
#split_name = "ContractType"
param = """Normal count vector with max 200. New submission which is repeatable.
and nicer
Extra_columns: %s
Splitted on %s and learned separately with extraTree
""" % (",".join(columns), split_name)
col_index = columns.index(split_name)
feature_category = extra_features[col_index]
validation_features_category = extra_valid_features[col_index]
#features = dio.join_features("%s_" + type_n + "_count_vector_matrix_max_f_200",
#["Title", "FullDescription", "LocationRaw"],
#extra_features)
#validation_features = dio.join_features("%s_" + type_v + "_count_vector_matrix_max_f_200",
#["Title", "FullDescription", "LocationRaw"],
#extra_valid_features).astype(np.int64)
features = dio.join_features("%s_" + type_n + "_" + short_id + "_matrix",
tfidf_columns,
extra_features)
validation_features = dio.join_features("%s_" + type_v + "_" + short_id + "_matrix",
tfidf_columns,
extra_valid_features)
salaries = dio.get_salaries(type_n, log=True).astype(np.int64)
if not submission:
valid_salaries = dio.get_salaries(type_v, log=True)
best_predictions = dio.get_prediction(model_name="ExtraTree_min_sample2_20trees_200f_noNorm_categoryTimeType_log", type_n="valid")
par = " classed from 0-11500 then 4 classes to 100 000 and to end NoNormal classTypeTime"
def encode_salaries(salaries, bins):
bin_edges = np.linspace(11500.0, 100000, bins + 1, endpoint=True)
#hist, bin_edges = np.histogram(salaries, bins)
print np.diff(bin_edges)
idxs = np.searchsorted(bin_edges, salaries, side="right")
return idxs
#salaries_enc = encode_salaries(salaries, 4)
#valid_salaries_enc = encode_salaries(valid_salaries, 4)
print salaries.shape
metric = dio.error_metric
for bins in [4]: #range(10,15):
n_trees = 20
par = " Splited by category NoNormal classTypeTime salaries and valid"
name = "ExtraTree_min_sample%d_%dtrees_200f_noNorm_categoryTimeType_count_%s_split_new_log" % (min_samples_split, n_trees, split_name)
print name
num_classes = len(le_features[col_index].classes_)
print "classes:", num_classes
print le_features[col_index].classes_
param += "\nclasses: %d\n" % num_classes
def predict(class_id, param):
print "predicting: ", class_id
param += "\npredicting: %s\n" % (le_features[col_index].classes_[class_id],)
salaries_idx = np.where(feature_category == class_id)
valid_idx = np.where(validation_features_category == class_id)
param += "Salaries len: %d, valid len: %d\n" % (len(salaries_idx[0]), len(valid_idx[0]))
if len(salaries_idx[0]) == 0 or len(valid_idx[0]) == 0:
return [], None, param
classifier = ExtraTreesRegressor(n_estimators=n_trees,
verbose=0,
n_jobs=4, # 2 jobs on submission / 4 on valid test
oob_score=False,
min_samples_split=min_samples_split,
random_state=3465343)
print features[salaries_idx[0], :].shape
print salaries[salaries_idx].shape
print validation_features[0].shape
classifier.fit(features[salaries_idx[0], :], salaries[salaries_idx])
predictions_part = classifier.predict(validation_features[valid_idx[0]])
return predictions_part, valid_idx, param
predictions = np.zeros_like(valid_salaries)
for cur_class_id in range(num_classes):
predictions_part, idx, param = predict(cur_class_id, param)
if idx is not None:
predictions[idx] = predictions_part
mae_pred = metric(valid_salaries[idx], predictions_part)
mae_best_pred = metric(valid_salaries[idx], best_predictions[idx])
if mae_pred < mae_best_pred:
isbetter = "DA"
else:
isbetter = "nope"
ppara = "Curr MAE: %0.2f Best MAE: %0.2f %s\n" % (mae_pred, mae_best_pred, isbetter)
print ppara
param += ppara
if submission:
dio.save_prediction(name, predictions, type_n=type_v)
dio.write_submission(name + ".csv", predictions=predictions)
else:
dio.compare_valid_pred(valid_salaries, predictions)
metric = dio.error_metric
mae = metric(valid_salaries, predictions)
print "MAE validation: ", mae
dio.save_model(ExtraTreesRegressor(n_estimators=n_trees, min_samples_split=min_samples_split, random_state=3465343), name, mae, parameters=param)
dio.save_prediction(name, predictions, type_n=type_v)
#oob_predictions = classifier.oob_prediction_
#mae_oob = mean_absolute_error(salaries, oob_predictions)
#print "MAE OOB: ", mae_oob
#classifier1 = ExtraTreesRegressor(n_estimators=n_trees,
#verbose=1,
#n_jobs=3,
#oob_score=False,
#min_samples_split=min_samples_split,
#random_state=3465343)
#scores = cross_val_score(classifier1, features, salaries, cv=3, score_func=metric, verbose=1)
#print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
#mae_cv = "%0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
#dio.save_model(classifier, name, mae_cv=mae_cv, parameters=param)