| Overall Statistics |
|
Total Trades 113 Average Win 3.21% Average Loss -4.15% Compounding Annual Return 4.724% Drawdown 41.900% Expectancy 0.109 Net Profit 25.977% Sharpe Ratio 0.319 Probabilistic Sharpe Ratio 4.641% Loss Rate 38% Win Rate 62% Profit-Loss Ratio 0.77 Alpha 0.076 Beta -0.147 Annual Standard Deviation 0.168 Annual Variance 0.028 Information Ratio -0.384 Tracking Error 0.257 Treynor Ratio -0.365 Total Fees $2855.31 |
import pandas as pd
import numpy as np
import timeseriespps as tspps
from sklearn.model_selection import TimeSeriesSplit as tss
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.pipeline import Pipeline
from scipy.stats import zscore
from statistics import mode
import warnings
warnings.filterwarnings("ignore")
import re
from mlfinlab import fracdiff as fd
import pipegridmodels as pgm
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import f1_score
class PPSSelector:
def __init__(self,
algorithm,
factors,
targets,
pasts=[3, 5, 10, 15, 22, 66],
futures=[5,10, 15, 22],
emas = [5, 15, 22, 56],
training_period=1825):
self.algorithm = algorithm
self.training_period = training_period
self.factors = factors
self.targets = targets
self.pasts = pasts
self.futures = futures
self.emas = emas
self.models = False
self.is_trained = False
self.threshold = False
self.best_target = False
self.best_predictors = False
self.prediction_duration = False
def get_pps_selection(self):
factors = self.factors
targets = self.targets
algorithm = self.algorithm
futures = self.futures
pasts = self.pasts
symbols = list(algorithm.ActiveSecurities.Keys)
history = algorithm.History(symbols, self.training_period, Resolution.Daily)
tickers = {str(symbol): str(symbol.ID) for symbol in symbols}
inv_symbols = dict(map(reversed, tickers.items()))
# Treatment of the price:
price_history = history['close'].unstack(level=0)
price_history.columns = [inv_symbols[col] for col in price_history.columns]
price_history = pd.DataFrame(price_history)
# Treatment of the volume:
volume_history = history['volume'].unstack(level=0)
volume_history.columns = ['VOL_'+inv_symbols[col] for col in volume_history.columns]
volume_history = pd.DataFrame(volume_history)
# Treatment of Fracdiffs:
diff_price = fd.frac_diff(price_history, 0.3, ).dropna()
diff_price.columns = ['FD_'+col for col in diff_price.columns]
diff_price = pd.DataFrame(diff_price)
# Join all
price_history = price_history.join(volume_history).join(diff_price)
# Valid factors:
valid_prices = [value for value in price_history.columns if value in factors]
valid_volumes = [value for value in volume_history.columns if value[4:] in factors]
valid_difss = list(diff_price.columns)
valid_factors = valid_prices + valid_volumes + valid_difss
for column in price_history[valid_factors]:
for frame in pasts:
price_history['P_'+str(frame)+"_"+column+'_PAST'] = price_history[column].shift(frame)
price_history['R_'+str(frame)+"_"+column+'_PAST'] = price_history[column].pct_change(frame)
for ema in self.emas:
price_history['EMA'+str(ema)+'_'+str(frame)+"_"+column+'_PAST'] = price_history[column].shift(frame).ewm(span=ema).mean()
for column in price_history[targets]:
for frame in futures:
price_history['D_'+str(frame)+"_"+column+'_FUT'] = -price_history[column].pct_change(-frame).dropna() > 0
price_history.dropna(inplace=True)
pipe = [('1',StandardScaler()), ('2', MinMaxScaler())]
all_predictors = pd.DataFrame(columns=['x','y','ppscore'])
future_cols = [col for col in price_history.columns if 'FUT' in col and 'D_' in col]
past_cols = [col for col in price_history.columns if 'PAST' in col]
for column in future_cols:
predictors_df = tspps.predictors(price_history[past_cols + [column]].dropna(),
y=column,
pipeline=pipe,
time_series=True,
cross_validation=tss(4))
all_predictors = all_predictors.append(predictors_df[['x','y','ppscore']])
sorted_predictors = all_predictors.sort_values('ppscore', ascending=False).reset_index(drop=True)
try:
sorted_predictors['std_ppscore'] = sorted_predictors[['ppscore']].apply(zscore)
except:
algorithm.Debug("Zscore Error")
return -1, -1
cut_off_devs = 3
pred_type = 'D_'
good_predictors = sorted_predictors[sorted_predictors['std_ppscore'] > cut_off_devs]
good_predictors = good_predictors[ good_predictors['y'].str.contains(pred_type) ].reset_index(drop=True)
good_targets = good_predictors.groupby('y')[['std_ppscore']].mean()
good_targets.sort_values('std_ppscore', ascending=False, inplace=True)
INDEX = 0
self.best_target = good_targets.iloc[INDEX].name
self.best_predictors = list(good_predictors[good_predictors['y'] == self.best_target]['x'])
self.prediction_duration = int(re.findall(r'\d+', self.best_target)[0])
# Generate data:
data = price_history[self.best_predictors + [self.best_target]]
total_samples = len(data)
X = data[self.best_predictors]
y = data[self.best_target].astype(bool)
self.models = [pgm.RfcPipeGrid().model, pgm.SVCPipeGrid().model , pgm.MlpPipeGrid().model ]
#self.models = [pgm.SVCPipeGrid().model]
# Fit the random search model
for estimator in self.models:
try: estimator.fit(X, y)
except:
algorithm.Debug('Estimator Fit Error')
return -1, -1
probabilities = []
thresh_values = np.arange(0, 1, 0.01)
thresholds = []
def to_labels(pos_probs, threshold):
return (pos_probs >= threshold).astype('int')
for model in self.models:
pred = model.predict(X)[-1]
proba = model.predict_proba(X)
probabilities.append(proba[-1][1])
# Model threshold:
probs = proba[:, 1]
# define thresholds
scores_f1 = [f1_score(y, to_labels(probs, t), average='weighted') for t in thresh_values]
scores_roc_auc = [roc_auc_score(y, to_labels(probs, t), average='weighted') for t in thresh_values]
scores_accuracy = [accuracy_score(y, to_labels(probs, t)) for t in thresh_values]
ix_f1 = np.argmax(scores_f1)
ix_roc_auc = np.argmax(scores_roc_auc)
ix_acc = np.argmax(scores_accuracy)
all_scores = [thresh_values[ix_f1], thresh_values[ix_roc_auc], thresh_values[ix_acc] ]
mean_thresh = np.mean([all_scores[0]])
thresholds.append(mean_thresh)
predictions = []
for i in range(len(self.models)):
if probabilities[i] > thresholds[i]: predictions.append(1)
else: predictions.append(0)
prediction = mode(predictions)
time = algorithm.Time.date()
algorithm.Debug(str(time)+' Predicted: '+str(prediction))
algorithm.Debug(str( pred ))
return prediction, 1from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier as rfc
from sklearn.neural_network import MLPClassifier as mlp
from sklearn.svm import SVC
from sklearn.model_selection import TimeSeriesSplit
from sklearn.preprocessing import StandardScaler, MinMaxScaler
class MlpPipeGrid:
def __init__(self):
model_type = mlp()
scalers = [('scaler1', StandardScaler() ),
('scaler2', MinMaxScaler() ),]
pipeline = Pipeline([*scalers, ('model', model_type )])
search_grid = {'model__hidden_layer_sizes': [(50,50,50), (50,100,50), (100,)],
'model__activation': ['tanh', 'relu'],
'model__solver': ['sgd', 'adam'],
'model__alpha': [0.0001, 0.05],
'model__learning_rate': ['constant','adaptive'],
}
scoring = 'f1_weighted'
tscv = TimeSeriesSplit(n_splits=5)
self.model = GridSearchCV(estimator=pipeline,
param_grid=search_grid,
cv=tscv,
scoring=scoring)
class SVCPipeGrid:
def __init__(self):
model_type = SVC()
scalers = [('scaler1', StandardScaler() ),
('scaler2', MinMaxScaler() ),]
pipeline = Pipeline([*scalers, ('model', model_type )])
search_grid = {'model__C':[1,10,100,1000],
'model__gamma':[1,0.1,0.001,0.0001],
'model__kernel':['linear','rbf'],
'model__probability': [True]}
scoring = 'f1_weighted'
tscv = TimeSeriesSplit(n_splits=5)
self.model = GridSearchCV(estimator=pipeline,
param_grid=search_grid,
cv=tscv,
scoring=scoring)
class RfcPipeGrid:
def __init__(self):
model_type = rfc()
scalers = [('scaler1', StandardScaler() ),
('scaler2', MinMaxScaler() ),]
pipeline = Pipeline([*scalers, ('model', model_type )])
search_grid = { 'model__n_estimators': [50, 100, 200],
'model__max_features': ['auto'],
'model__max_depth': [5, 10, None],
'model__min_samples_split': [5, 10],
'model__min_samples_leaf': [1, 2, 4],
'model__bootstrap': [True]}
scoring = 'f1_weighted'
tscv = TimeSeriesSplit(n_splits=5)
self.model = GridSearchCV(estimator=pipeline,
param_grid=search_grid,
cv=tscv,
scoring=scoring)from PPSSelector import PPSSelector
class PPSPredictor(QCAlgorithm):
def Initialize(self):
N_YEARS = 5
test_length = timedelta(days= int(365 * N_YEARS)+1)
self.SetStartDate(datetime.today() - test_length)
self.SetEndDate(datetime.today())
self.SetCash(1000000)
# Hourly and Daily resolutions are posible, minute data minimizes
# 'stale data' warning messages.
resolution = Resolution.Daily
# Add Required Symbols
factors_0 = ['XLK', 'XLY', 'XLB', 'XLV', 'XLP', 'XLI', 'XLU', 'XLF', 'XLE'] #XLC Dropped
factors_1 = ['TLT', 'QQQ', 'SPY']
factors_2 = ['QQQ']
factor_list = [factors_0, factors_1, factors_2]
index = int(self.GetParameter("index"))
factors = factor_list[index]
targets = ['SPY']
hedges = []
symbols = [self.AddEquity(ticker, resolution).Symbol for ticker in factors + targets + hedges]
self.SetBenchmark("SPY")
self.UniverseSettings.Resolution = resolution
self.SetBrokerageModel(AlphaStreamsBrokerageModel())
self.AddAlpha(PPSAlphaModel(factors,
targets,
hedges,
resolution))
self.SetExecution(ImmediateExecutionModel())
self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel(lambda time: None))
# self.SetRiskManagement(TrailingStopRiskManagementModel(0.025))
class PPSAlphaModel(AlphaModel):
"""
"""
def __init__(self, factors, targets, hedges, resolution):
self.Name = "PPSAlphaModel"
# Factor, prediction target and instruments to exploit the predictions.
self.factors = factors
self.targets = targets
self.hedges = hedges
# Dynamically allow for hour/day/minute resolutions:
self.resolution = resolution
# Parameters for machine learning model:
# ML model intial training and retraining:
self.selector = False
self.training_period = 252*5 # Now passed into history as data points
# Other operational parameters:
self.time_slack = 10 # Minutes after market open.
self.expected_gain = 0.05 # Fixed value, the model is unable predict this.
# Directions dictionary:
self.directions = [InsightDirection.Down,
InsightDirection.Up,
InsightDirection.Flat]
def Update(self, algorithm, data):
# Initialize every slice of data:
insights = []
if not data.HasData:
return []
# Obtain the market operating time:
hours = algorithm.ActiveSecurities[self.targets[0]].Exchange.Hours
# Positions are entered as market opens:
previous_day = algorithm.Time - timedelta(days=int(True))
slack = timedelta(minutes=self.time_slack)
operating_time = hours.GetNextMarketOpen(previous_day, False) + slack
# If invested, wait for the insight to decay:
if (algorithm.Time.hour == 10 and algorithm.Time.minute == 0) or self.resolution == Resolution.Daily:
for target in self.targets:
if algorithm.Securities[target].Invested: return[]
self.selector = PPSSelector(algorithm,
self.factors,
self.targets,
training_period=self.training_period)
prediction, probability = self.selector.get_pps_selection()
if prediction == -1: return []
tdelta = timedelta(days=self.selector.prediction_duration)
insight_duration = hours.GetNextMarketClose(algorithm.Time + tdelta,
False) - algorithm.Time
direction_instrument = self.directions[int(prediction)]
# TODO: Alternative bet_size to be implemented in the future:
# Sorry for codetag.
# bet_size = (probability-0.5)*2
bet_size = 1
for target in self.targets:
if not data.ContainsKey(target): continue
insights.append(Insight(target,
insight_duration,
InsightType.Price,
direction_instrument,
self.expected_gain,
probability,
self.Name,
bet_size))
for hedge in self.hedges:
if not data.ContainsKey(hedge): continue
insights.append(Insight(hedge,
insight_duration,
InsightType.Price,
direction_instrument,
self.expected_gain,
probability,
self.Name,
1-bet_size))
return insights
return []import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
def plot_df(df, color='blue', size=(16, 7), legend='Close Price', y_label='Price in USD'):
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
ax = df.plot()
plt.ylabel(y_label)
x = 0.01
y = 0.01
plt.text(x, y, 'www.ostirion.net', fontsize=15, transform=ax.transAxes)
plt.legend(ncol=int(len(df.columns) / 2))
date_form = mdates.DateFormatter("%m-%Y")
plt.xticks(rotation=45);
plt.show()
def plot_corr_hm(df, title='Title', size=(16, 7), annot = True):
corr = df.corr()
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.color_palette("RdBu")
ax = sns.heatmap(corr, mask=mask, vmax=.3, center=0, cmap=cmap, annot=annot,
square=True, linewidths=0, cbar_kws={"shrink": .5}, fmt='g')
ax.set_title(title)
plt.setp(ax.get_yticklabels(), rotation=0);
plt.setp(ax.get_xticklabels(), rotation=90);
plt.show()
def plot_cm(df, title='Title', size=(16,7)):
plt.style.use('dark_background')
plt.rcParams["figure.figsize"] = size
cmap = sns.color_palette("Blues")
ax = sns.heatmap(df, cmap=cmap, annot=True, linewidths=0, cbar_kws={"shrink": .5}, fmt='g')
ax.set_title(title)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.setp(ax.get_xticklabels(), rotation=0);# Import Modified PPS module:
from sklearn import tree
from sklearn import preprocessing
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_error, f1_score
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.pipeline import Pipeline
import pandas as pd
from pandas.api.types import (
is_numeric_dtype,
is_bool_dtype,
is_object_dtype,
is_categorical_dtype,
is_string_dtype,
is_datetime64_any_dtype,
is_timedelta64_dtype,
)
NOT_SUPPORTED_ANYMORE = "NOT_SUPPORTED_ANYMORE"
TO_BE_CALCULATED = -1
def _calculate_model_cv_score_(
df, target, feature, task, cross_validation, random_seed, **kwargs
):
"Calculates the mean model score based on cross-validation"
# Sources about the used methods:
# https://scikit-learn.org/stable/modules/tree.html
# https://scikit-learn.org/stable/modules/cross_validation.html
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html
metric = task["metric_key"]
model = task["model"]
# shuffle the rows - this is important for cross-validation
# because the cross-validation just takes the first n lines
# if there is a strong pattern in the rows eg 0,0,0,0,1,1,1,1
# then this will lead to problems because the first cv sees mostly 0 and the later 1
# this approach might be wrong for timeseries because it might leak information
# Fixing the time_series:
if not TIME_SERIES:
df = df.sample(frac=1, random_state=random_seed, replace=False)
# preprocess target
if task["type"] == "classification":
label_encoder = preprocessing.LabelEncoder()
df[target] = label_encoder.fit_transform(df[target])
target_series = df[target]
else:
target_series = df[target]
# preprocess feature
if _dtype_represents_categories(df[feature]):
one_hot_encoder = preprocessing.OneHotEncoder()
array = df[feature].__array__()
sparse_matrix = one_hot_encoder.fit_transform(array.reshape(-1, 1))
feature_input = sparse_matrix
else:
# reshaping needed because there is only 1 feature
feature_input = df[feature].values.reshape(-1, 1)
# Cross-validation is stratifiedKFold for classification, KFold for regression
# CV on one core (n_job=1; default) has shown to be fastest
try:
scores = cross_val_score(
model, feature_input, target_series, cv=cross_validation, scoring=metric
)
except:
return 0
return scores.mean()
def _normalized_mae_score(model_mae, naive_mae):
"Normalizes the model MAE score, given the baseline score"
# # Value range of MAE is [0, infinity), 0 is best
# 10, 5 ==> 0 because worse than naive
# 10, 20 ==> 0.5
# 5, 20 ==> 0.75 = 1 - (mae/base_mae)
if model_mae > naive_mae:
return 0
else:
return 1 - (model_mae / naive_mae)
def _mae_normalizer(df, y, model_score, **kwargs):
"In case of MAE, calculates the baseline score for y and derives the PPS."
df["naive"] = df[y].median()
baseline_score = mean_absolute_error(df[y], df["naive"]) # true, pred
ppscore = _normalized_mae_score(abs(model_score), baseline_score)
return ppscore, baseline_score
def _normalized_f1_score(model_f1, baseline_f1):
"Normalizes the model F1 score, given the baseline score"
# # F1 ranges from 0 to 1
# # 1 is best
# 0.5, 0.7 ==> 0 because model is worse than naive baseline
# 0.75, 0.5 ==> 0.5
#
if model_f1 < baseline_f1:
return 0
else:
scale_range = 1.0 - baseline_f1 # eg 0.3
f1_diff = model_f1 - baseline_f1 # eg 0.1
return f1_diff / scale_range # 0.1/0.3 = 0.33
def _f1_normalizer(df, y, model_score, random_seed):
"In case of F1, calculates the baseline score for y and derives the PPS."
label_encoder = preprocessing.LabelEncoder()
df["truth"] = label_encoder.fit_transform(df[y])
df["most_common_value"] = df["truth"].value_counts().index[0]
random = df["truth"].sample(frac=1, random_state=random_seed)
baseline_score = max(
f1_score(df["truth"], df["most_common_value"], average="weighted"),
f1_score(df["truth"], random, average="weighted"),
)
ppscore = _normalized_f1_score(model_score, baseline_score)
return ppscore, baseline_score
TIME_SERIES = False
VALID_CALCULATIONS = {
"regression": {
"type": "regression",
"is_valid_score": True,
"model_score": TO_BE_CALCULATED,
"baseline_score": TO_BE_CALCULATED,
"ppscore": TO_BE_CALCULATED,
"metric_name": "mean absolute error",
"metric_key": "neg_mean_absolute_error",
"model": tree.DecisionTreeRegressor(),
"score_normalizer": _mae_normalizer,
},
"classification": {
"type": "classification",
"is_valid_score": True,
"model_score": TO_BE_CALCULATED,
"baseline_score": TO_BE_CALCULATED,
"ppscore": TO_BE_CALCULATED,
"metric_name": "weighted F1",
"metric_key": "f1_weighted",
"model": tree.DecisionTreeClassifier(),
"score_normalizer": _f1_normalizer,
},
"predict_itself": {
"type": "predict_itself",
"is_valid_score": True,
"model_score": 1,
"baseline_score": 0,
"ppscore": 1,
"metric_name": None,
"metric_key": None,
"model": None,
"score_normalizer": None,
},
"target_is_constant": {
"type": "target_is_constant",
"is_valid_score": True,
"model_score": 1,
"baseline_score": 1,
"ppscore": 0,
"metric_name": None,
"metric_key": None,
"model": None,
"score_normalizer": None,
},
"target_is_id": {
"type": "target_is_id",
"is_valid_score": True,
"model_score": 0,
"baseline_score": 0,
"ppscore": 0,
"metric_name": None,
"metric_key": None,
"model": None,
"score_normalizer": None,
},
"feature_is_id": {
"type": "feature_is_id",
"is_valid_score": True,
"model_score": 0,
"baseline_score": 0,
"ppscore": 0,
"metric_name": None,
"metric_key": None,
"model": None,
"score_normalizer": None,
},
}
INVALID_CALCULATIONS = [
"target_is_datetime",
"target_data_type_not_supported",
"empty_dataframe_after_dropping_na",
"unknown_error",
]
def _dtype_represents_categories(series) -> bool:
"Determines if the dtype of the series represents categorical values"
return (
is_bool_dtype(series)
or is_object_dtype(series)
or is_string_dtype(series)
or is_categorical_dtype(series)
)
def _determine_case_and_prepare_df(df, x, y, sample=5_000, random_seed=123):
"Returns str with the name of the determined case based on the columns x and y"
if x == y:
return df, "predict_itself"
df = df[[x, y]]
# IDEA: log.warning when values have been dropped
df = df.dropna()
if len(df) == 0:
return df, "empty_dataframe_after_dropping_na"
# IDEA: show warning
# raise Exception(
# "After dropping missing values, there are no valid rows left"
# )
df = _maybe_sample(df, sample, random_seed=random_seed)
if _feature_is_id(df, x):
return df, "feature_is_id"
category_count = df[y].value_counts().count()
if category_count == 1:
# it is helpful to separate this case in order to save unnecessary calculation time
return df, "target_is_constant"
if _dtype_represents_categories(df[y]) and (category_count == len(df[y])):
# it is important to separate this case in order to save unnecessary calculation time
return df, "target_is_id"
if _dtype_represents_categories(df[y]):
return df, "classification"
if is_numeric_dtype(df[y]):
# this check needs to be after is_bool_dtype (which is part of _dtype_represents_categories) because bool is considered numeric by pandas
return df, "regression"
if is_datetime64_any_dtype(df[y]) or is_timedelta64_dtype(df[y]):
# IDEA: show warning
# raise TypeError(
# f"The target column {y} has the dtype {df[y].dtype} which is not supported. A possible solution might be to convert {y} to a string column"
# )
return df, "target_is_datetime"
# IDEA: show warning
# raise Exception(
# f"Could not infer a valid task based on the target {y}. The dtype {df[y].dtype} is not yet supported"
# ) # pragma: no cover
return df, "target_data_type_not_supported"
def _feature_is_id(df, x):
"Returns Boolean if the feature column x is an ID"
if not _dtype_represents_categories(df[x]):
return False
category_count = df[x].value_counts().count()
return category_count == len(df[x])
def _maybe_sample(df, sample, random_seed=None):
"""
Maybe samples the rows of the given df to have at most `sample` rows
If sample is `None` or falsy, there will be no sampling.
If the df has fewer rows than the sample, there will be no sampling.
Parameters
----------
df : pandas.DataFrame
Dataframe that might be sampled
sample : int or `None`
Number of rows to be sampled
random_seed : int or `None`
Random seed that is forwarded to pandas.DataFrame.sample as `random_state`
Returns
-------
pandas.DataFrame
DataFrame after potential sampling
"""
if sample and len(df) > sample:
# this is a problem if x or y have more than sample=5000 categories
# TODO: dont sample when the problem occurs and show warning
df = df.sample(sample, random_state=random_seed, replace=False)
return df
def _is_column_in_df(column, df):
try:
return column in df.columns
except:
return False
def _score(
df, x, y, task, sample, cross_validation, random_seed, invalid_score, catch_errors
):
df, case_type = _determine_case_and_prepare_df(
df, x, y, sample=sample, random_seed=random_seed
)
task = _get_task(case_type, invalid_score)
if case_type in ["classification", "regression"]:
model_score = _calculate_model_cv_score_(
df,
target=y,
feature=x,
task=task,
cross_validation=cross_validation,
random_seed=random_seed,
)
# IDEA: the baseline_scores do sometimes change significantly, e.g. for F1 and thus change the PPS
# we might want to calculate the baseline_score 10 times and use the mean in order to have less variance
ppscore, baseline_score = task["score_normalizer"](
df, y, model_score, random_seed=random_seed
)
else:
model_score = task["model_score"]
baseline_score = task["baseline_score"]
ppscore = task["ppscore"]
return {
"x": x,
"y": y,
"ppscore": ppscore,
"case": case_type,
"is_valid_score": task["is_valid_score"],
"metric": task["metric_name"],
"baseline_score": baseline_score,
"model_score": abs(model_score), # sklearn returns negative mae
"model": task["model"],
}
def score(
df,
x,
y,
pipeline = [],
time_series = False,
task=NOT_SUPPORTED_ANYMORE,
sample=5_000,
cross_validation=4,
random_seed=123,
invalid_score=0,
catch_errors=True,
):
"""
Calculate the Predictive Power Score (PPS) for "x predicts y"
The score always ranges from 0 to 1 and is data-type agnostic.
A score of 0 means that the column x cannot predict the column y better than a naive baseline model.
A score of 1 means that the column x can perfectly predict the column y given the model.
A score between 0 and 1 states the ratio of how much potential predictive power the model achieved compared to the baseline model.
Parameters
----------
df : pandas.DataFrame
Dataframe that contains the columns x and y
x : str
Name of the column x which acts as the feature
y : str
Name of the column y which acts as the target
sample : int or `None`
Number of rows for sampling. The sampling decreases the calculation time of the PPS.
If `None` there will be no sampling.
cross_validation : int
Number of iterations during cross-validation. This has the following implications:
For example, if the number is 4, then it is possible to detect patterns when there are at least 4 times the same observation. If the limit is increased, the required minimum observations also increase. This is important, because this is the limit when sklearn will throw an error and the PPS cannot be calculated
random_seed : int or `None`
Random seed for the parts of the calculation that require random numbers, e.g. shuffling or sampling.
If the value is set, the results will be reproducible. If the value is `None` a new random number is drawn at the start of each calculation.
invalid_score : any
The score that is returned when a calculation is invalid, e.g. because the data type was not supported.
catch_errors : bool
If `True` all errors will be catched and reported as `unknown_error` which ensures convenience. If `False` errors will be raised. This is helpful for inspecting and debugging errors.
Returns
-------
Dict
A dict that contains multiple fields about the resulting PPS.
The dict enables introspection into the calculations that have been performed under the hood
"""
global TIME_SERIES
if time_series: TIME_SERIES = True
else: TIME_SERIES = False
global VALID_CALCULATIONS
if pipeline:
VALID_CALCULATIONS['regression']['model'] = Pipeline(pipeline + [('tree', tree.DecisionTreeRegressor())] )
VALID_CALCULATIONS['classification']['model'] = Pipeline( pipeline + [('tree', tree.DecisionTreeClassifier())] )
else:
VALID_CALCULATIONS['regression']['model'] = tree.DecisionTreeRegressor()
VALID_CALCULATIONS['classification']['model'] = tree.DecisionTreeClassifier()
if not isinstance(df, pd.DataFrame):
raise TypeError(
f"The 'df' argument should be a pandas.DataFrame but you passed a {type(df)}\nPlease convert your input to a pandas.DataFrame"
)
if not _is_column_in_df(x, df):
raise ValueError(
f"The 'x' argument should be the name of a dataframe column but the variable that you passed is not a column in the given dataframe.\nPlease review the column name or your dataframe"
)
if len(df[[x]].columns) >= 2:
raise AssertionError(
f"The dataframe has {len(df[[x]].columns)} columns with the same column name {x}\nPlease adjust the dataframe and make sure that only 1 column has the name {x}"
)
if not _is_column_in_df(y, df):
raise ValueError(
f"The 'y' argument should be the name of a dataframe column but the variable that you passed is not a column in the given dataframe.\nPlease review the column name or your dataframe"
)
if len(df[[y]].columns) >= 2:
raise AssertionError(
f"The dataframe has {len(df[[y]].columns)} columns with the same column name {y}\nPlease adjust the dataframe and make sure that only 1 column has the name {y}"
)
if task is not NOT_SUPPORTED_ANYMORE:
raise AttributeError(
"The attribute 'task' is no longer supported because it led to confusion and inconsistencies.\nThe task of the model is now determined based on the data types of the columns. If you want to change the task please adjust the data type of the column.\nFor more details, please refer to the README"
)
if random_seed is None:
from random import random
random_seed = int(random() * 1000)
try:
return _score(
df,
x,
y,
task,
sample,
cross_validation,
random_seed,
invalid_score,
catch_errors,
)
except Exception as exception:
if catch_errors:
case_type = "unknown_error"
task = _get_task(case_type, invalid_score)
return {
"x": x,
"y": y,
"ppscore": task["ppscore"],
"case": case_type,
"is_valid_score": task["is_valid_score"],
"metric": task["metric_name"],
"baseline_score": task["baseline_score"],
"model_score": task["model_score"], # sklearn returns negative mae
"model": task["model"],
}
else:
raise exception
def _get_task(case_type, invalid_score):
if case_type in VALID_CALCULATIONS.keys():
return VALID_CALCULATIONS[case_type]
elif case_type in INVALID_CALCULATIONS:
return {
"type": case_type,
"is_valid_score": False,
"model_score": invalid_score,
"baseline_score": invalid_score,
"ppscore": invalid_score,
"metric_name": None,
"metric_key": None,
"model": None,
"score_normalizer": None,
}
raise Exception(f"case_type {case_type} is not supported")
def _format_list_of_dicts(scores, output, sorted):
"""
Format list of score dicts `scores`
- maybe sort by ppscore
- maybe return pandas.Dataframe
- output can be one of ["df", "list"]
"""
if sorted:
scores.sort(key=lambda item: item["ppscore"], reverse=True)
if output == "df":
df_columns = [
"x",
"y",
"ppscore",
"case",
"is_valid_score",
"metric",
"baseline_score",
"model_score",
"model",
]
data = {column: [score[column] for score in scores] for column in df_columns}
scores = pd.DataFrame.from_dict(data)
return scores
def predictors(df, y, output="df", pipeline = [], time_series=False, sorted=True, **kwargs):
"""
Calculate the Predictive Power Score (PPS) of all the features in the dataframe
against a target column
Parameters
----------
df : pandas.DataFrame
The dataframe that contains the data
y : str
Name of the column y which acts as the target
output: str - potential values: "df", "list"
Control the type of the output. Either return a pandas.DataFrame (df) or a list with the score dicts
sorted: bool
Whether or not to sort the output dataframe/list by the ppscore
pipeline: list
list of transformstions to be included in the processing of the data
kwargs:
Other key-word arguments that shall be forwarded to the pps.score method,
e.g. `sample, `cross_validation, `random_seed, `invalid_score`, `catch_errors`
Returns
-------
pandas.DataFrame or list of Dict
Either returns a tidy dataframe or a list of all the PPS dicts. This can be influenced
by the output argument
"""
if not isinstance(df, pd.DataFrame):
raise TypeError(
f"The 'df' argument should be a pandas.DataFrame but you passed a {type(df)}\nPlease convert your input to a pandas.DataFrame"
)
if not _is_column_in_df(y, df):
raise ValueError(
f"The 'y' argument should be the name of a dataframe column but the variable that you passed is not a column in the given dataframe.\nPlease review the column name or your dataframe"
)
if len(df[[y]].columns) >= 2:
raise AssertionError(
f"The dataframe has {len(df[[y]].columns)} columns with the same column name {y}\nPlease adjust the dataframe and make sure that only 1 column has the name {y}"
)
if not output in ["df", "list"]:
raise ValueError(
f"""The 'output' argument should be one of ["df", "list"] but you passed: {output}\nPlease adjust your input to one of the valid values"""
)
if not sorted in [True, False]:
raise ValueError(
f"""The 'sorted' argument should be one of [True, False] but you passed: {sorted}\nPlease adjust your input to one of the valid values"""
)
scores = [score(df, column, y, pipeline, time_series, **kwargs) for column in df if column != y]
return _format_list_of_dicts(scores=scores, output=output, sorted=sorted)
def matrix(df, output="df", sorted=False, **kwargs):
"""
Calculate the Predictive Power Score (PPS) matrix for all columns in the dataframe
Parameters
----------
df : pandas.DataFrame
The dataframe that contains the data
output: str - potential values: "df", "list"
Control the type of the output. Either return a pandas.DataFrame (df) or a list with the score dicts
sorted: bool
Whether or not to sort the output dataframe/list by the ppscore
kwargs:
Other key-word arguments that shall be forwarded to the pps.score method,
e.g. `sample, `cross_validation, `random_seed, `invalid_score`, `catch_errors`
Returns
-------
pandas.DataFrame or list of Dict
Either returns a tidy dataframe or a list of all the PPS dicts. This can be influenced
by the output argument
"""
if not isinstance(df, pd.DataFrame):
raise TypeError(
f"The 'df' argument should be a pandas.DataFrame but you passed a {type(df)}\nPlease convert your input to a pandas.DataFrame"
)
if not output in ["df", "list"]:
raise ValueError(
f"""The 'output' argument should be one of ["df", "list"] but you passed: {output}\nPlease adjust your input to one of the valid values"""
)
if not sorted in [True, False]:
raise ValueError(
f"""The 'sorted' argument should be one of [True, False] but you passed: {sorted}\nPlease adjust your input to one of the valid values"""
)
scores = [score(df, x, y, **kwargs) for x in df for y in df]
return _format_list_of_dicts(scores=scores, output=output, sorted=sorted)