def _get_train_costs(self):
# Get train scores
monary = Monary(host=self.mongo_host)
iterations, loss, source_id = monary.query(
db=self.mongo_db,
coll='train_scores',
query={'experiment_id': self.experiment_id},
fields=['iteration', 'loss', 'source_id'],
types=['int32', 'float32', 'int8']
)
scores_df = pd.DataFrame(
{'loss': loss, 'source_id': source_id}, index=iterations)
scores_df = scores_df.sort_index()
return scores_df
def _get_train_costs(self):
# Get train scores
monary = Monary(host=self.mongo_host)
iterations, loss, source_id = monary.query(
db=self.mongo_db,
coll='train_scores',
query={'experiment_id': self.experiment_id},
fields=['iteration', 'loss', 'source_id'],
types=['int32', 'float32', 'int8'])
scores_df = pd.DataFrame({
'loss': loss,
'source_id': source_id
},
index=iterations)
scores_df = scores_df.sort_index()
return scores_df
def _plot_validation_scores_for_source_and_fold(self, ax, source_id, fold,
show_axes_labels,
show_scales):
fields = ['iteration'] + ['scores.' + metric_name for metric_name in
self.validation_metric_names]
monary = Monary(host=self.mongo_host)
result = monary.query(
db=self.mongo_db,
coll='validation_scores',
query={
'experiment_id': self.experiment_id,
'source_id': source_id,
'fold': fold
},
fields=fields,
types=['int32'] + ['float32'] * len(self.validation_metric_names)
)
index = result[0]
data = {metric_name: result[i+1] for i, metric_name in
enumerate(self.validation_metric_names)}
df = pd.DataFrame(data, index=index)
df = df.sort_index()
df = self._downsample(df)
# Create multiple independent axes. Adapted from Joe Kington's answer:
# http://stackoverflow.com/a/7734614
# Colours
n = len(self.validation_metric_names)
colors = get_colors(n)
# Twin the x-axis to make independent y-axes.
axes = [ax]
for metric_name in self.validation_metric_names[1:]:
axes.append(ax.twinx())
SEP = 0.2
if show_scales:
for i, axis in enumerate(axes):
axis.yaxis.tick_right()
if i != 0:
# To make the border of the right-most axis visible,
# we need to turn the frame on. This hides the other plots,
# however, so we need to turn its fill off.
axis.set_frame_on(True)
axis.patch.set_visible(False)
# Move the last y-axes spines over to the right.
axis.spines['right'].set_position(
('axes', 1 + (SEP * i)))
else:
for axis in axes:
axis.tick_params(labelright=False, labelleft=False)
axis.yaxis.set_ticks_position('none')
axis.spines['right'].set_visible(False)
for axis in axes:
for spine in ['top', 'left', 'bottom']:
axis.spines[spine].set_visible(False)
axis.xaxis.set_ticks_position('none')
lines = []
for i, (axis, metric_name, color) in enumerate(
zip(axes, self.validation_metric_names, colors)):
axis.tick_params(axis='y', colors=color, direction='out')
label = metric_name.replace("regression.", "")
label = label.replace("classification_", "")
label = label.replace("_", " ")
label = label.replace(".", " ")
label = label.replace(" ", "\n")
line, = axis.plot(
df.index, df[metric_name].values, color=color, label=label)
if show_axes_labels and show_scales:
axis.set_ylabel(
label, color=color, rotation=0, fontsize=8, va='bottom')
if i == 0:
coords = (1.05, 1.1)
else:
coords = (1.05 + (SEP * i), 1.1)
axis.yaxis.set_label_coords(*coords)
lines.append(line)
self._last_iteration_processed['validation'] = index[-1]
return lines
from monary import Monary
import numpy as np
import pandas as pd
import time
mon = Monary()
columns = [
'properties.total_residential_units',
'properties.total_job_spaces',
'properties.parcel_id',
'properties.max_dua',
'properties.max_far'
]
t1 = time.time()
numpy_arrays = mon.query(
'togethermap',
'places',
{'collectionId': 'ZC7yyAyA8jkDFnRtf'},
columns,
['float32']*len(columns)
)
df = np.matrix(numpy_arrays).transpose()
df = pd.DataFrame(df, columns=columns)
print time.time()-t1
print df.describe()
from monary import Monary
import numpy as np
import pandas as pd
import time
mon = Monary()
columns = [
'properties.total_residential_units', 'properties.total_job_spaces',
'properties.parcel_id', 'properties.max_dua', 'properties.max_far'
]
t1 = time.time()
numpy_arrays = mon.query('togethermap', 'places',
{'collectionId': 'ZC7yyAyA8jkDFnRtf'}, columns,
['float32'] * len(columns))
df = np.matrix(numpy_arrays).transpose()
df = pd.DataFrame(df, columns=columns)
print time.time() - t1
print df.describe()
def _plot_validation_scores_for_source_and_fold(self, ax, source_id, fold,
show_axes_labels,
show_scales):
fields = ['iteration'] + [
'scores.' + metric_name
for metric_name in self.validation_metric_names
]
monary = Monary(host=self.mongo_host)
result = monary.query(db=self.mongo_db,
coll='validation_scores',
query={
'experiment_id': self.experiment_id,
'source_id': source_id,
'fold': fold
},
fields=fields,
types=['int32'] +
['float32'] * len(self.validation_metric_names))
index = result[0]
data = {
metric_name: result[i + 1]
for i, metric_name in enumerate(self.validation_metric_names)
}
df = pd.DataFrame(data, index=index)
df = df.sort_index()
df = self._downsample(df)
# Create multiple independent axes. Adapted from Joe Kington's answer:
# http://stackoverflow.com/a/7734614
# Colours
n = len(self.validation_metric_names)
colors = get_colors(n)
# Twin the x-axis to make independent y-axes.
axes = [ax]
for metric_name in self.validation_metric_names[1:]:
axes.append(ax.twinx())
SEP = 0.2
if show_scales:
for i, axis in enumerate(axes):
axis.yaxis.tick_right()
if i != 0:
# To make the border of the right-most axis visible,
# we need to turn the frame on. This hides the other plots,
# however, so we need to turn its fill off.
axis.set_frame_on(True)
axis.patch.set_visible(False)
# Move the last y-axes spines over to the right.
axis.spines['right'].set_position(('axes', 1 + (SEP * i)))
else:
for axis in axes:
axis.tick_params(labelright=False, labelleft=False)
axis.yaxis.set_ticks_position('none')
axis.spines['right'].set_visible(False)
for axis in axes:
for spine in ['top', 'left', 'bottom']:
axis.spines[spine].set_visible(False)
axis.xaxis.set_ticks_position('none')
lines = []
for i, (axis, metric_name, color) in enumerate(
zip(axes, self.validation_metric_names, colors)):
axis.tick_params(axis='y', colors=color, direction='out')
label = metric_name.replace("regression.", "")
label = label.replace("classification_", "")
label = label.replace("_", " ")
label = label.replace(".", " ")
label = label.replace(" ", "\n")
line, = axis.plot(df.index,
df[metric_name].values,
color=color,
label=label)
if show_axes_labels and show_scales:
axis.set_ylabel(label,
color=color,
rotation=0,
fontsize=8,
va='bottom')
if i == 0:
coords = (1.05, 1.1)
else:
coords = (1.05 + (SEP * i), 1.1)
axis.yaxis.set_label_coords(*coords)
lines.append(line)
self._last_iteration_processed['validation'] = index[-1]
return lines
#script to save csv with comment data
import pymongo, urllib3
from pymongo import MongoClient
from hpfunctions import getUrl, stripWhite,stripWhiteList
from lxml import html
client=MongoClient()
db=client['hp']
import pandas
import numpy
from monary import Monary
mon=Monary()
print 'available columns'
for i in db.comStats.find()[0]:print i
columns = ['typos', 'avSyllables', 'nPunct']
numpy_arrays = mon.query('hp',
'comStats',
{},
columns,
['int32', 'int32', 'int32:20'])
df = numpy.matrix(numpy_arrays).transpose()
df = pandas.DataFrame(df, columns=columns)
print 'starting to write file pandasTest.csv'
df.to_csv('pandasTest.csv', sep='\t')