def test_make_array():
test1 = ds.make_array(0)
assert len(test1) == 1
test2 = ds.make_array(2, 3, 4)
assert sum(test2) == 9
assert test2.dtype == "int64"
test3 = ds.make_array("foo", "bar")
assert test3.dtype == "<U3"
def game_filter(csv_file):
# Takes in a csv filepath of one of the EightThirtyFour data sets
# and filters the data to games with a 10 or less point lead in
# the last 6 minutes of the game.
pbp = Table().read_table(csv_file)
unique_games = pbp.group('GAME_ID').column(0)
print(unique_games)
last_quarter = pbp.where('PERIOD', predicates.are.equal_to(4))
transformed_minutes = last_quarter.apply(time_string_to_number,
'PCTIMESTRING')
last_quarter_and_minutes = last_quarter.with_column(
'TIME', transformed_minutes)
between_six_and_seven = last_quarter_and_minutes.where(
'TIME', predicates.are.below_or_equal_to(6.5))
close_games = make_array()
for game in unique_games:
game_scores_only = between_six_and_seven.where(
'GAME_ID',
predicates.are.equal_to(game)).select('TIME', 'SCORE').where(
'SCORE', predicates.are.not_equal_to('nan'))
score = game_scores_only.row(0).item(1)
t1, t2 = score.split('-')
if abs(int(t1) - int(t2)) <= 10:
close_games = np.append(close_games, game)
return close_games
def test_sample_proportions():
uniform = ds.sample_proportions(1000, np.ones(50) / 50)
assert len(uniform) == 50 and _round_eq(1, sum(uniform))
assert [
x in (0, 0.5, 1)
for x in ds.sample_proportions(2, ds.make_array(.2, .3, .5))
]
def test_proportions_from_distribution():
t = ds.Table().with_column('probs', np.ones(50) / 50)
u = ds.proportions_from_distribution(t, 'probs', 1000)
assert t.num_columns == 1 and t.num_rows == 50
assert u.num_columns == 2 and u.num_rows == 50
uniform = u.column(1)
assert len(uniform) == 50 and _round_eq(1, sum(uniform))
assert [
x in (0, 0.5, 1)
for x in ds.sample_proportions(2, ds.make_array(.2, .3, .5))
]
def conditional_dist(self, label, given='', show_ev=False):
"""
Given the random variable label, finds the conditional distribution of
the other variable.
Parameters
----------
label : String
Variable given.
Returns
-------
JointDistribution Table
Examples
--------
>>> coins = Table().values('Coin1', ['H', 'T'], 'Coin2', ['H','T']).probabilities(np.array([0.24, 0.36, 0.16,0.24])).to_joint()
>>> coins.conditional_dist('Coin1', 'Coin2')
Coin1=H Coin1=T Sum
Dist. of Coin1 | Coin2=H 0.6 0.4 1.0
Dist. of Coin1 | Coin2=T 0.6 0.4 1.0
Marginal of Coin1 0.6 0.4 1.0
>>> coins.conditional_dist('Coin2', 'Coin1')
Dist. of Coin2 | Coin1=H Dist. of Coin2 | Coin1=T Marginal of Coin2
Coin2=H 0.4 0.4 0.4
Coin2=T 0.6 0.6 0.6
Sum 1.0 1.0 1.0
"""
# TODO Refactor this function.
if label == self._Y_column_label:
both = self.both_marginals()
new = np.append(both.index[0:-1], 'Sum')
y = both.apply(conditional, axis=0).set_index(new)
matrix = y.to_numpy()[:-1, :]
y_labels = list(self.index)
domain = np.array([evaluate(lab) for lab in y_labels])
exp_values = [
sum(matrix[:, i] * domain) for i in range(len(matrix[0]))
]
column_names = y.columns
new = make_array()
for i in np.arange(len(column_names) - 1):
new_name = 'Dist. of {0} | '.format(self._Y_column_label)
new_name += column_names[i]
new = np.append(new, new_name)
new = np.append(new,
'Marginal of {0}'.format(self._Y_column_label))
y.columns = new
if show_ev:
y.loc['EV'] = exp_values
return y
elif label == self._X_column_label:
both = self.both_marginals()
x = both.apply(conditional, axis=1).rename(
columns={
'Sum: Marginal of {0}'.format(self._Y_column_label): 'Sum'
})
matrix = x.to_numpy()[:, :-1]
x_labels = list(self)
domain = np.array([evaluate(lab) for lab in x_labels])
exp_values = [sum(matrix[i] * domain) for i in range(len(matrix))]
indices = both.index
new = make_array()
for i in np.arange(len(indices) - 1):
new_name = 'Dist. of {0} | '.format(self._X_column_label)
new_name += indices[i]
new = np.append(new, new_name)
new = np.append(new,
'Marginal of {0}'.format(self._X_column_label))
new_df = x.set_index(new)
if show_ev:
new_df['EV'] = exp_values
return new_df
else:
raise AssertionError(
'Label does not correspond with existing variable name')
