def make_column_table(cities, data):
def append_detail_line(ct, city_name, prices):
ct.append_detail(
city=city_name,
mean=prices.mean(),
median=prices.median(),
stddev=prices.std(),
count=len(prices),
)
ct = ColumnsTable((
('city', 30, '%30s', ('city'), 'name of city'),
('mean', 7, '%7.0f', ('mean'), 'mean price across time periods'),
('median', 7, '%7.0f', ('median'),
'median price across time periods'),
('stddev', 7, '%7.0f', ('stddev'),
'standard deviation of prices across time periods'),
('count', 7, '%7.0f', ('count'),
'number of transactions across time periods'),
))
for city in cities:
in_city = data.city == city
city_data = data[in_city]
prices = city_data.price
append_detail_line(ct, city, prices)
# summary line is across all the cities
append_detail_line(ct, '* all cities *', data.price)
ct.append_legend()
return ct
class ReportWithColumnsTable(object):
def __init__(self,
header_lines,
column_defs,
print_as_spaces,
verbose=True):
self._report = Report()
self._header(header_lines)
self._ct = ColumnsTable(column_defs, verbose)
self._print_as_spaces = print_as_spaces
def _header(self, header_lines):
for line in header_lines:
self._report.append(line)
def append_detail(self, **kwds):
# replace NaN with None
with_spaces = {
k: (None if self._print_as_spaces(k, v) else v)
for k, v in kwds.iteritems()
}
self._ct.append_detail(**with_spaces)
def write(self, path):
self._t.append_legend()
for line in self._t.iterlines():
self._report.append(line)
self._report.write(path)
def __init__(self,
header_lines,
column_defs,
print_as_spaces,
verbose=True):
self._report = Report()
self._header(header_lines)
self._ct = ColumnsTable(column_defs, verbose)
self._print_as_spaces = print_as_spaces
def make_details(data, test_months, n_best, n_worst):
'return a ColumnTable'
extra_info = []
feature_names = Features().ege_names(control.arg.features)
columns_table = ColumnsTable((
('test_month', 6, '%6s', ('test', 'month'), 'test month'),
('nth', 2, '%2d', (' ', 'n'), 'rank of feature (1 ==> more frequently included)'),
('probability', 4, '%4.1f', (' ', 'prob'), 'probability feature appears in a decision tree'),
('feature_name', 40, '%40s', (' ', 'feature name'), 'name of feature'),
),
verbose=True)
for test_month in test_months:
value = data[ReductionKey(test_month)]
if 'feature_importances' not in value.importances:
# one month has an ensemble model
# skip that month
print 'chart a sees an unexpected ensemble model'
print 'test_month', test_month
print 'value', value
print 'value.importance', value.importances
print 'skipping the test month'
print 'entering debugger'
pdb.set_trace()
importances = value.importances['feature_importances']
assert value.importances['features_group'] == control.arg.features, value
model = value.model
assert type(model) == ResultKeyGbr or type(model) == ResultKeyRfr
sorted_indices = importances.argsort() # sorted first lowest, last highest
for nth_best in xrange(n_best):
if nth_best == len(feature_names):
break
index = sorted_indices[len(importances) - nth_best - 1]
columns_table.append_detail(
test_month=test_month,
nth=nth_best + 1,
probability=importances[index] * 100.0,
feature_name=feature_names[index]
)
extra_info.append([test_month, nth_best+1, importances[index]*100.0, feature_names[index]])
for nth in xrange(n_worst):
break # skip, for now
if nth == len(feature_names):
break
nth_worst = n_worst - nth - 1
index = sorted_indices[nth_worst]
columns_table.append_detail(
test_month=test_month,
nth=len(importances) - nth_worst,
probability=importances[index] * 100.0,
feature_name=feature_names[index]
)
if n_best > 1 or n_worst > 1:
# insert blank line between test_months if more than 1 row in a month
columns_table.append_detail()
columns_table.append_legend()
return columns_table, extra_info
def __init__(self, k, validation_month, ensemble_weighting,
column_definitions, test):
self._column_definitions = column_definitions
self._report = Report()
self._test = test
self._header(k, validation_month, ensemble_weighting)
cd = self._column_definitions.defs_for_columns(
'description',
'mae_validation',
'mae_query',
'mare_validation',
'mare_query',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
def make_details(data, test_months):
"return a ColumnTable"
columns_table = ColumnsTable(
(
("mean_prob", 5, "%5.2f", ("mean", "prob"), "mean probability feature appears in a decision tree"),
("feature_name", 40, "%40s", (" ", "feature name"), "name of feature"),
),
verbose=True,
)
mean_importance = make_mean_importance_by_feature(test_months)
for feature_name in sorted(mean_importance, key=mean_importance.get, reverse=True):
columns_table.append_detail(mean_prob=mean_importance[feature_name] * 100.0, feature_name=feature_name)
columns_table.append_legend()
return columns_table
def __init__(self, validation_month, k, column_definitions, test):
self._report = Report()
self._header(validation_month, k)
self._column_definitions = column_definitions
self._test = test
cd = self._column_definitions.defs_for_columns(
'median_absolute_error',
'model',
'n_months_back',
'max_depth',
'n_estimators',
'max_features',
'learning_rate',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
class ChartCDReport(object):
def __init__(self, column_definitions, test):
self._column_definitions = column_definitions
self._test = test
self._report = Report()
cd = self._column_definitions.defs_for_columns(
'validation_month',
'rank',
'median_absolute_error',
'median_price',
'model',
'n_months_back',
'max_depth',
'n_estimators',
'max_features',
'learning_rate',
'alpha',
'l1_ratio',
'units_X',
'units_y',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
self._header()
def append(self, line):
self._report.append(line)
def write(self, path):
self._ct.append_legend()
for line in self._ct.iterlines():
self._report.append(line)
if self._test:
self._report.append('** TESTING: DISCARD')
self._report.write(path)
def _header(self):
self._report.append(
'Median Absolute Error (MAE) by month for best-performing models and their hyperparameters'
)
self._report.append(' ')
def append_detail(self, **kwds):
with_spaces = {
k:
(None if self._column_definitions.replace_by_spaces(k, v) else v)
for k, v in kwds.iteritems()
}
self._ct.append_detail(**with_spaces)
def __init__(self, k, ensemble_weighting, column_definitions, test):
self._column_definitions = column_definitions
self._test = test
self._report = Report()
self._header(k, ensemble_weighting)
cd = self._column_definitions.defs_for_columns(
'validation_month',
'mae_index0',
'mae_ensemble',
'mae_best_next_month',
'median_price',
'fraction_median_price_next_month_index0',
'fraction_median_price_next_month_ensemble',
'fraction_median_price_next_month_best',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
def make_report(title, ordered_cities):
def make_detail_line(city):
return {
'city': city,
'median_price': median_prices[city],
'median_price_index': median_prices_indices[city],
'n_trades': n_trades[city],
'n_trades_index': n_trades_indices[city],
}
c = ColumnsTable((
('city', 30, '%30s', ('', '', '', '', '', 'City'), 'city name'),
('median_price', 7, '%7.0f', ('', '', '', '', 'median', 'price'),
'median price in city'),
('median_price_index', 7, '%7.2f', ('median', 'price', '/',
'overall', 'median', 'price'),
'median price as fraction of overall median price'),
('n_trades', 7, '%7.0f', ('', '', '', '', 'number', 'trades'),
'number of trades across all months'),
('n_trades_index', 7, '%7.2f', ('number', 'trades', '/ ',
'overall', 'median', 'trades'),
'median number trades as fraction of overall median number of trades'
),
))
for city in ordered_cities:
c.append_detail(**make_detail_line(city))
c.append_legend(40)
r = Report()
r.append(title)
r.append(' ')
for line in c.iterlines():
r.append(line)
return r
def make_details(data, control):
'return a ColumnsTable'
def append_feature_group_description(ct):
ct.append_line(' ')
ct.append_line('Features groups;')
ct.append_line('s : only size features')
ct.append_line('sw : only size and wealth features')
ct.append_line('swp : only size, wealth, and property features')
ct.append_line('swpn : all features: size, wealth, property, and neighborhood')
ct = ColumnsTable((
('month', 6, '%6s', ('', 'month'), 'training month'),
('features', 8, '%8s', ('features', 'group'), 'group of features'),
('model', 5, '%5s', ('best', 'model'), 'family of best model'),
('mae', 6, '%6.0f', ('', 'mae'), 'mae of best model in month using features'),
),
verbose=True,
)
for month in control.test_months:
for features in control.feature_groups:
mae_model = data[month][features]
ct.append_detail(
month=month,
features=features,
model=mae_model.model,
mae=mae_model.mae,
)
ct.append_detail() # blank line separates each month
ct.append_legend()
append_feature_group_description(ct)
return ct
class ChartEReport(object):
def __init__(self, k, ensemble_weighting, column_definitions, test):
self._column_definitions = column_definitions
self._test = test
self._report = Report()
self._header(k, ensemble_weighting)
cd = self._column_definitions.defs_for_columns(
'validation_month',
'model',
'n_months_back',
'n_estimators',
'max_features',
'max_depth',
'learning_rate',
'rank',
'weight',
'mae_validation',
'mae_query',
'mae_ensemble',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
def write(self, path):
self._ct.append_legend()
for line in self._ct.iterlines():
self._report.append(line)
if self._test:
self._report.append('** TESTING: DISCARD')
self._report.write(path)
def detail_line(self, **kwds):
with_spaces = {
k:
(None if self._column_definitions.replace_by_spaces(k, v) else v)
for k, v in kwds.iteritems()
}
self._ct.append_detail(**with_spaces)
def _header(self, k, ensemble_weighting):
self._report.append(
'Performance of Best Models Separately and as an Ensemble')
self._report.append(' ')
self._report.append('Considering Best K = %d models' % k)
self._report.append('Ensemble weighting: %s' % ensemble_weighting)
def __init__(self, k, ensemble_weighting, column_definitions, test):
self._column_definitions = column_definitions
self._test = test
self._report = Report()
self._header(k, ensemble_weighting)
cd = self._column_definitions.defs_for_columns(
'validation_month',
'model',
'n_months_back',
'n_estimators',
'max_features',
'max_depth',
'learning_rate',
'rank',
'weight',
'mae_validation',
'mae_query',
'mae_ensemble',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
class ChartBReport(object):
def __init__(self, validation_month, k, column_definitions, test):
self._report = Report()
self._header(validation_month, k)
self._column_definitions = column_definitions
self._test = test
cd = self._column_definitions.defs_for_columns(
'median_absolute_error',
'model',
'n_months_back',
'max_depth',
'n_estimators',
'max_features',
'learning_rate',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
def _header(self, validation_month, k):
def a(line):
self._report.append(line)
a('MAE for %d best-performing models and their hyperparameters' % k)
a('Validation month: %s' % validation_month)
a(' ')
def append_detail(self, **kwds):
# replace NaN with None
with_spaces = {
k:
(None if self._column_definitions.replace_by_spaces(k, v) else v)
for k, v in kwds.iteritems()
}
self._ct.append_detail(**with_spaces)
def write(self, path):
self._ct.append_legend()
for line in self._ct.iterlines():
self._report.append(line)
if self._test:
self._report.append('**TESTING: DISCARD')
self._report.write(path)
class ChartFReport(object):
def __init__(self, k, ensemble_weighting, column_definitions, test):
self._column_definitions = column_definitions
self._test = test
self._report = Report()
self._header(k, ensemble_weighting)
cd = self._column_definitions.defs_for_columns(
'validation_month',
'mae_index0',
'mae_ensemble',
'mae_best_next_month',
'median_price',
'fraction_median_price_next_month_index0',
'fraction_median_price_next_month_ensemble',
'fraction_median_price_next_month_best',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
def write(self, path):
self._ct.append_legend()
for line in self._ct.iterlines():
self._report.append(line)
if self._test:
self._report.append('** TESTING: DISCARD')
self._report.write(path)
def detail_line(self, **kwds):
with_spaces = {
k:
(None if self._column_definitions.replace_by_spaces(k, v) else v)
for k, v in kwds.iteritems()
}
self._ct.append_detail(**with_spaces)
def _header(self, k, ensemble_weighting):
self._report.append(
'Comparison of Errors of Ensemble and Best Model That Know the Future'
)
self._report.append(' ')
self._report.append('Considering Best K = %d models' % k)
self._report.append('Ensemble weighting: %s' % ensemble_weighting)
def make_details(data, test_months, n_best, n_worst):
"return a ColumnTable"
feature_names = Features().ege_names(control.arg.features)
columns_table = ColumnsTable(
(
("test_month", 6, "%6s", ("test", "month"), "test month"),
("nth", 2, "%2d", (" ", "n"), "rank of feature (1 ==> more frequently included)"),
("probability", 4, "%4.1f", (" ", "prob"), "probability feature appears in a decision tree"),
("feature_name", 40, "%40s", (" ", "feature name"), "name of feature"),
),
verbose=True,
)
for test_month in test_months:
value = data[ReductionKey(test_month)]
importances = value.importances["feature_importances"]
assert value.importances["features_group"] == control.arg.features, value
model = value.model
assert type(model) == ResultKeyGbr or type(model) == ResultKeyRfr
sorted_indices = importances.argsort() # sorted first lowest, last highest
for nth_best in xrange(n_best):
if nth_best == len(feature_names):
break
index = sorted_indices[len(importances) - nth_best - 1]
columns_table.append_detail(
test_month=test_month,
nth=nth_best + 1,
probability=importances[index] * 100.0,
feature_name=feature_names[index],
)
for nth in xrange(n_worst):
break # skip, for now
if nth == len(feature_names):
break
nth_worst = n_worst - nth - 1
index = sorted_indices[nth_worst]
columns_table.append_detail(
test_month=test_month,
nth=len(importances) - nth_worst,
probability=importances[index] * 100.0,
feature_name=feature_names[index],
)
if n_best > 1 or n_worst > 1:
# insert blank line between test_months if more than 1 row in a month
columns_table.append_detail()
columns_table.append_legend()
return columns_table
def __init__(self, column_definitions, test):
self._column_definitions = column_definitions
self._test = test
self._report = Report()
cd = self._column_definitions.defs_for_columns(
'validation_month',
'rank',
'median_absolute_error',
'median_price',
'model',
'n_months_back',
'max_depth',
'n_estimators',
'max_features',
'learning_rate',
'alpha',
'l1_ratio',
'units_X',
'units_y',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
self._header()
def make_table_stats(data, control, in_report_p):
'return Report with statistics for years and months that obey the filter'
r = Report()
r.append('Prices by Month')
r.append('')
ct = ColumnsTable((
('year', 4, '%4d', (' ', ' ', 'year'), 'year of transaction'),
('month', 5, '%5d', (' ', ' ', 'month'), 'month of transaction'),
('mean_price', 6, '%6.0f', (' ', ' mean', 'price'),
'mean price in dollars'),
('median_price', 6, '%6.0f', (' ', 'median', 'price'),
'median price in dollars'),
('mean_price_ratio', 6, '%6.3f', (' mean', ' price', ' ratio'),
'ratio of price in current month to prior month'),
('median_price_ratio', 6, '%6.3f', ('median', ' price', ' ratio'),
'ratio of price in current month to prior month'),
('number_trades', 6, '%6d', ('number', 'of', 'trades'),
'number of trades in the month'),
))
prior_mean_price = None
prior_median_price = None
for year in (2003, 2004, 2005, 2006, 2007, 2008, 2009):
for month in (1, 2, 3) if year == 2009 else (1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12):
if in_report_p(year, month):
selected = data.month == Month(year, month)
prices = data[selected].price
mean_price = prices.mean()
median_price = prices.median()
number_trades = len(prices)
ct.append_detail(
year=year,
month=month,
mean_price=mean_price,
median_price=median_price,
mean_price_ratio=None if prior_mean_price is None else
mean_price / prior_mean_price,
median_price_ratio=None if prior_median_price is None else
median_price / prior_median_price,
number_trades=number_trades,
)
prior_mean_price = mean_price
prior_median_price = median_price
ct.append_legend()
for line in ct.iterlines():
r.append(line)
return r
def make_details(data, control):
'return a ColumnsTable'
def append_feature_group_description(ct):
ct.append_line(' ')
ct.append_line('Features groups;')
ct.append_line('s : only size features')
ct.append_line('sw : only size and wealth features')
ct.append_line('swp : only size, wealth, and property features')
ct.append_line(
'swpn : all features: size, wealth, property, and neighborhood'
)
ct = ColumnsTable(
(
('month', 6, '%6s', ('', 'month'), 'training month'),
('features', 8, '%8s',
('features', 'group'), 'group of features'),
('model', 5, '%5s', ('best', 'model'), 'family of best model'),
('mae', 6, '%6.0f',
('', 'mae'), 'mae of best model in month using features'),
),
verbose=True,
)
my_info = []
for month in control.test_months:
for features in control.feature_groups:
mae_model = data[month][features]
ct.append_detail(
month=month,
features=features,
model=mae_model.model,
mae=mae_model.mae,
)
my_info.append(
[month, features, mae_model.model, mae_model.mae])
ct.append_detail() # blank line separates each month
ct.append_legend()
append_feature_group_description(ct)
return ct, my_info
def make_column_table(df):
ct = ColumnsTable(columns=(
('city', 30, '%30s', ('', 'city'), 'city in Los Angeles Country'),
('count', 6, '%6d', (' ', 'count'),
'number of transactions in 2007'),
('median_price', 7, '%7.0f', ('median', 'price'), 'median price'),
), )
for index, series in df.iterrows():
ct.append_detail(
city=series['city'],
count=series['count'],
median_price=series['median_price'],
)
ct.append_legend()
return ct
class ChartHReport(object):
def __init__(self, k, validation_month, ensemble_weighting,
column_definitions, test):
self._column_definitions = column_definitions
self._report = Report()
self._test = test
self._header(k, validation_month, ensemble_weighting)
cd = self._column_definitions.defs_for_columns(
'description',
'mae_validation',
'mae_query',
'mare_validation',
'mare_query',
)
self._ct = ColumnsTable(columns=cd, verbose=True)
def write(self, path):
self._ct.append_legend()
for line in self._ct.iterlines():
self._report.append(line)
if self._test:
self._report.append('** TESTING: DISCARD')
self._report.write(path)
def detail_line(self, **kwds):
with_spaces = {
k:
(None if self._column_definitions.replace_by_spaces(k, v) else v)
for k, v in kwds.iteritems()
}
self._ct.append_detail(**with_spaces)
def preformatted_line(self, line):
print line
self._ct.append_line(line)
def _header(self, k, validation_month, ensemble_weighting):
self._report.append(
'Performance of Best Models Separately and as an Ensemble')
self._report.append(' ')
self._report.append('Considering Best K = %d models' % k)
self._report.append('For validation month %s' % validation_month)
self._report.append('Ensemble weighting: %s' % ensemble_weighting)
def make_details(data, test_months):
'return a ColumnTable'
columns_table = ColumnsTable((
('mean_prob', 5, '%5.2f', ('mean', 'prob'), 'mean probability feature appears in a decision tree'),
('feature_name', 40, '%40s', (' ', 'feature name'), 'name of feature'),
),
verbose=True)
my_prob = []
my_featname = []
mean_importance = make_mean_importance_by_feature(test_months)
for feature_name in sorted(mean_importance, key=mean_importance.get, reverse=True):
columns_table.append_detail(
mean_prob=mean_importance[feature_name] * 100.0,
feature_name=feature_name,
)
if mean_importance[feature_name] * 100.0 >= 1:
my_prob.append(mean_importance[feature_name] * 100.0)
my_featname.append(feature_name)
columns_table.append_legend()
return columns_table, my_featname, my_prob
def make_chart_stats(data, control, filter_f):
'return Report with statistics for years and months that obey the filter'
r = Report()
r.append('Prices by Month')
r.append('')
ct = ColumnsTable((
('year', 4, '%4d', (' ', ' ', 'year'), 'year of transaction'),
('month', 5, '%5d', (' ', ' ', 'month'), 'month of transaction'),
('mean_price', 6, '%6.0f', (' ', ' mean', 'price'), 'mean price in dollars'),
('median_price', 6, '%6.0f', (' ', 'median', 'price'), 'median price in dollars'),
('mean_price_ratio', 6, '%6.3f', (' mean', ' price', ' ratio'), 'ratio of price in current month to prior month'),
('median_price_ratio', 6, '%6.3f', ('median', ' price', ' ratio'), 'ratio of price in current month to prior month'),
('number_trades', 6, '%6d', ('number', 'of', 'trades'), 'number of trades in the month'),
))
prior_mean_price = None
prior_median_price = None
for year in xrange(2003, 2010):
for month in xrange(1, 13):
if filter_f(year, month):
value = data[make_reduction_key(year, month)]
mean_price = value['mean']
median_price = value['median']
number_trades = value['count']
ct.append_detail(
year=year,
month=month,
mean_price=mean_price,
median_price=median_price,
mean_price_ratio=None if prior_mean_price is None else mean_price / prior_mean_price,
median_price_ratio=None if prior_median_price is None else median_price / prior_median_price,
number_trades=number_trades,
)
prior_mean_price = mean_price
prior_median_price = median_price
ct.append_legend()
for line in ct.iterlines():
r.append(line)
return r
