def test_update_dictionary():
"""Tests for update_dictionary"""
# no conflicts, takes union
default_dict = {"a": 1, "d": 4}
overwrite_dict = {"b": 2, "c": 3}
merged_dict = update_dictionary(default_dict,
overwrite_dict=overwrite_dict)
assert merged_dict == {"a": 1, "b": 2, "c": 3, "d": 4}
# overwrite takes precedence in conflicts
default_dict = {"a": 1, "d": 4}
overwrite_dict = {"a": 2, "c": 3}
merged_dict = update_dictionary(default_dict,
overwrite_dict=overwrite_dict)
assert merged_dict == {"a": 2, "c": 3, "d": 4}
# overwrite can be None or {}
default_dict = {"a": 1, "b": 4}
merged_dict = update_dictionary(default_dict, overwrite_dict=None)
assert merged_dict == default_dict
merged_dict = update_dictionary(default_dict, overwrite_dict={})
assert merged_dict == default_dict
default_dict = {"a": 1, "b": 4, "c": 5}
overwrite_dict = {"a": 1, "b": 4}
update_dictionary(default_dict,
overwrite_dict=overwrite_dict,
allow_unknown_keys=False)
# overwrite cannot be a strict superset of default
with pytest.raises(ValueError, match=r"Unexpected key\(s\) found"):
overwrite_dict = {"a": 1, "b": 4, "d": 1}
update_dictionary(default_dict,
overwrite_dict=overwrite_dict,
allow_unknown_keys=False)
def fit(self, X, y=None):
"""Updates `self.impute_params`.
Parameters
----------
X : `pandas.DataFrame`
Training input data. e.g. each column is a timeseries.
Columns are expected to be numeric.
y : None
There is no need of a target in a transformer, yet the pipeline API
requires this parameter.
Returns
-------
self : object
Returns self.
"""
assert isinstance(X, pd.DataFrame)
self._is_fitted = True
# sets default parameters
if self.impute_algorithm is not None:
default_params = DEFAULT_PARAMS.get(self.impute_algorithm, {})
self.impute_params = update_dictionary(default_params, overwrite_dict=self.impute_params)
return self
def fit(self,
X,
y=None,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
**fit_params):
"""Fits ``Silverkite`` forecast model.
Parameters
----------
X: `pandas.DataFrame`
Input timeseries, with timestamp column,
value column, and any additional regressors.
The value column is the response, included in
``X`` to allow transformation by `sklearn.pipeline`.
y: ignored
The original timeseries values, ignored.
(The ``y`` for fitting is included in ``X``).
time_col: `str`
Time column name in ``X``.
value_col: `str`
Value column name in ``X``.
fit_params: `dict`
additional parameters for null model.
Returns
-------
self : self
Fitted model is stored in ``self.model_dict``.
"""
# Initializes `fit_algorithm_dict` with default values.
# This cannot be done in __init__ to remain compatible
# with sklearn grid search.
default_fit_algorithm_dict = {
"fit_algorithm": "ridge",
"fit_algorithm_params": None
}
self.fit_algorithm_dict = update_dictionary(
default_fit_algorithm_dict, overwrite_dict=self.fit_algorithm_dict)
# Fits null model
super().fit(X=X,
y=y,
time_col=time_col,
value_col=value_col,
**fit_params)
self.model_dict = self.silverkite.forecast_simple(
df=X,
time_col=time_col,
value_col=value_col,
time_properties=self.time_properties,
freq=self.freq,
forecast_horizon=self.forecast_horizon,
origin_for_time_vars=self.origin_for_time_vars,
train_test_thresh=self.train_test_thresh,
training_fraction=self.training_fraction,
fit_algorithm=self.fit_algorithm_dict["fit_algorithm"],
fit_algorithm_params=self.
fit_algorithm_dict["fit_algorithm_params"],
holidays_to_model_separately=self.holidays_to_model_separately,
holiday_lookup_countries=self.holiday_lookup_countries,
holiday_pre_num_days=self.holiday_pre_num_days,
holiday_post_num_days=self.holiday_post_num_days,
holiday_pre_post_num_dict=self.holiday_pre_post_num_dict,
daily_event_df_dict=self.daily_event_df_dict,
changepoints_dict=self.changepoints_dict,
yearly_seasonality=self.yearly_seasonality,
quarterly_seasonality=self.quarterly_seasonality,
monthly_seasonality=self.monthly_seasonality,
weekly_seasonality=self.weekly_seasonality,
daily_seasonality=self.daily_seasonality,
max_daily_seas_interaction_order=self.
max_daily_seas_interaction_order,
max_weekly_seas_interaction_order=self.
max_weekly_seas_interaction_order,
autoreg_dict=self.autoreg_dict,
seasonality_changepoints_dict=self.seasonality_changepoints_dict,
min_admissible_value=self.min_admissible_value,
max_admissible_value=self.max_admissible_value,
uncertainty_dict=self.uncertainty_dict,
growth_term=self.growth_term,
regressor_cols=self.regressor_cols,
feature_sets_enabled=self.feature_sets_enabled,
extra_pred_cols=self.extra_pred_cols,
regression_weight_col=self.regression_weight_col,
simulation_based=self.simulation_based)
# Sets attributes based on ``self.model_dict``
super().finish_fit()
return self
def apply_prophet_model_components_defaults(self,
model_components=None,
time_properties=None):
"""Sets default values for ``model_components``.
Called by ``get_hyperparameter_grid`` after ``time_properties` is defined.
Requires ``time_properties`` as well as ``model_components``
so we do not simply override
`~greykite.framework.templates.forecast_config_defaults.ForecastConfigDefaults.apply_model_components_defaults`.
Parameters
----------
model_components : :class:`~greykite.framework.templates.autogen.forecast_config.ModelComponentsParam` or None, default None
Configuration of model growth, seasonality, events, etc.
See the docstring of this class for details.
time_properties : `dict` [`str`, `any`] or None, default None
Time properties dictionary (likely produced by
`~greykite.common.time_properties_forecast.get_forecast_time_properties`)
with keys:
``"period"`` : `int`
Period of each observation (i.e. minimum time between observations, in seconds).
``"simple_freq"`` : `SimpleTimeFrequencyEnum`
``SimpleTimeFrequencyEnum`` member corresponding to data frequency.
``"num_training_points"`` : `int`
Number of observations for training.
``"num_training_days"`` : `int`
Number of days for training.
``"start_year"`` : `int`
Start year of the training period.
``"end_year"`` : `int`
End year of the forecast period.
``"origin_for_time_vars"`` : `float`
Continuous time representation of the first date in ``df``.
If None, start_year is set to 2015 and end_year to 2030.
Returns
-------
model_components : :class:`~greykite.framework.templates.autogen.forecast_config.ModelComponentsParam`
The provided ``model_components`` with default values set
"""
if model_components is None:
model_components = ModelComponentsParam()
else:
# makes a copy to avoid mutating input
model_components = dataclasses.replace(model_components)
if time_properties is None:
time_properties = {
"start_year": 2015,
"end_year": 2030,
}
# seasonality
default_seasonality = {
"seasonality_mode": ["additive"],
"seasonality_prior_scale": [10.0],
"yearly_seasonality": ['auto'],
"weekly_seasonality": ['auto'],
"daily_seasonality": ['auto'],
"add_seasonality_dict": [None]
}
# If seasonality params are not provided, uses default params. Otherwise, prefers provided params.
# `allow_unknown_keys=False` requires `model_components.seasonality` keys to be a subset of
# `default_seasonality` keys.
model_components.seasonality = update_dictionary(
default_dict=default_seasonality,
overwrite_dict=model_components.seasonality,
allow_unknown_keys=False)
# growth
default_growth = {"growth_term": ["linear"]}
model_components.growth = update_dictionary(
default_dict=default_growth,
overwrite_dict=model_components.growth,
allow_unknown_keys=False)
# events
default_events = {
"holiday_lookup_countries":
"auto", # see `get_prophet_holidays` for defaults
"holiday_pre_num_days": [2],
"holiday_post_num_days": [2],
"start_year": time_properties["start_year"],
"end_year": time_properties["end_year"],
"holidays_prior_scale": [10.0]
}
model_components.events = update_dictionary(
default_dict=default_events,
overwrite_dict=model_components.events,
allow_unknown_keys=False)
# Creates events dictionary for prophet estimator
# Expands the range of holiday years by 1 year on each end, to ensure we have coverage of most relevant holidays.
year_list = list(
range(model_components.events["start_year"] - 1,
model_components.events["end_year"] + 2))
# Currently we support only one set of holiday_lookup_countries, holiday_pre_num_days and holiday_post_num_days.
# Shows a warning if user supplies >1 set.
if len(model_components.events["holiday_pre_num_days"]) > 1:
warnings.warn(
f"`events['holiday_pre_num_days']` list has more than 1 element. We currently support only 1 element. "
f"Using {model_components.events['holiday_pre_num_days'][0]}.")
if len(model_components.events["holiday_post_num_days"]) > 1:
warnings.warn(
f"`events['holiday_post_num_days']` list has more than 1 element. We currently support only 1 element. "
f"Using {model_components.events['holiday_post_num_days'][0]}."
)
# If events["holiday_lookup_countries"] has multiple options, picks the first option
if (model_components.events["holiday_lookup_countries"] is not None and
model_components.events["holiday_lookup_countries"] != "auto"):
if len(model_components.events["holiday_lookup_countries"]) > 1:
# There are multiple elements
if (any(
isinstance(x, list) for x in
model_components.events["holiday_lookup_countries"])
or None
in model_components.events["holiday_lookup_countries"]
or "auto" in
model_components.events["holiday_lookup_countries"]):
# Not a flat list of country names
warnings.warn(
f"`events['holiday_lookup_countries']` contains multiple options. "
f"We currently support only 1 option. Using {model_components.events['holiday_lookup_countries'][0]}."
)
model_components.events[
"holiday_lookup_countries"] = model_components.events[
"holiday_lookup_countries"][0]
elif isinstance(
model_components.events["holiday_lookup_countries"][0],
(list, tuple)):
# There's only one element, and it's a list of countries
model_components.events[
"holiday_lookup_countries"] = model_components.events[
"holiday_lookup_countries"][0]
model_components.events = {
"holidays_df":
self.get_prophet_holidays(
year_list=year_list,
countries=model_components.events["holiday_lookup_countries"],
# holiday effect is modeled from "holiday_pre_num_days" days before
# to "holiday_post_num_days" days after the holiday
lower_window=-model_components.events["holiday_pre_num_days"]
[0], # Prophet expects a negative value for `lower_window`
upper_window=model_components.events["holiday_post_num_days"]
[0]),
"holidays_prior_scale":
model_components.events["holidays_prior_scale"]
}
# changepoints_dict
default_changepoints = {
"changepoint_prior_scale": [0.05],
"changepoints": [None],
"n_changepoints": [25],
"changepoint_range": [0.8]
}
model_components.changepoints = update_dictionary(
default_dict=default_changepoints,
overwrite_dict=model_components.changepoints,
allow_unknown_keys=False)
# uncertainty
default_uncertainty = {
"mcmc_samples": [0],
"uncertainty_samples": [1000]
}
model_components.uncertainty = update_dictionary(
default_dict=default_uncertainty,
overwrite_dict=model_components.uncertainty,
allow_unknown_keys=False)
# regressors
default_regressors = {"add_regressor_dict": [None]}
model_components.regressors = update_dictionary(
default_dict=default_regressors,
overwrite_dict=model_components.regressors,
allow_unknown_keys=False)
# there are no custom parameters for Prophet
# sets to {} if None, for each item if
# `model_components.hyperparameter_override` is a list of dictionaries
model_components.hyperparameter_override = update_dictionaries(
{}, overwrite_dicts=model_components.hyperparameter_override)
return model_components
def apply_default_model_components(model_components=None,
time_properties=None):
"""Sets default values for ``model_components``.
Parameters
----------
model_components : :class:`~greykite.framework.templates.autogen.forecast_config.ModelComponentsParam` or None, default None
Configuration of model growth, seasonality, events, etc.
See :func:`~greykite.framework.templates.silverkite_templates.silverkite_template` for details.
time_properties : `dict` [`str`, `any`] or None, default None
Time properties dictionary (likely produced by
`~greykite.common.time_properties_forecast.get_forecast_time_properties`)
with keys:
``"period"`` : `int`
Period of each observation (i.e. minimum time between observations, in seconds).
``"simple_freq"`` : `SimpleTimeFrequencyEnum`
``SimpleTimeFrequencyEnum`` member corresponding to data frequency.
``"num_training_points"`` : `int`
Number of observations for training.
``"num_training_days"`` : `int`
Number of days for training.
``"start_year"`` : `int`
Start year of the training period.
``"end_year"`` : `int`
End year of the forecast period.
``"origin_for_time_vars"`` : `float`
Continuous time representation of the first date in ``df``.
Returns
-------
model_components : :class:`~greykite.framework.templates.autogen.forecast_config.ModelComponentsParam`
The provided ``model_components`` with default values set
"""
if model_components is None:
model_components = ModelComponentsParam()
else:
# makes a copy to avoid mutating input
model_components = dataclasses.replace(model_components)
# sets default values
default_seasonality = {
"fs_components_df": [
pd.DataFrame({
"name": ["tod", "tow", "tom", "toq", "toy"],
"period": [24.0, 7.0, 1.0, 1.0, 1.0],
"order": [3, 3, 1, 1, 5],
"seas_names":
["daily", "weekly", "monthly", "quarterly", "yearly"]
})
],
}
model_components.seasonality = update_dictionary(
default_seasonality,
overwrite_dict=model_components.seasonality,
allow_unknown_keys=False)
# model_components.growth must be empty.
# Pass growth terms via `extra_pred_cols` instead.
default_growth = {}
model_components.growth = update_dictionary(
default_growth,
overwrite_dict=model_components.growth,
allow_unknown_keys=False)
default_events = {
"daily_event_df_dict": [None],
}
model_components.events = update_dictionary(
default_events,
overwrite_dict=model_components.events,
allow_unknown_keys=False)
default_changepoints = {
"changepoints_dict": [None],
"seasonality_changepoints_dict": [None],
# Not allowed, to prevent leaking future information
# into the past. Pass `changepoints_dict` with method="auto" for
# automatic detection.
# "changepoint_detector": [None],
}
model_components.changepoints = update_dictionary(
default_changepoints,
overwrite_dict=model_components.changepoints,
allow_unknown_keys=False)
default_autoregression = {
"autoreg_dict": [None],
}
model_components.autoregression = update_dictionary(
default_autoregression,
overwrite_dict=model_components.autoregression,
allow_unknown_keys=False)
default_regressors = {}
model_components.regressors = update_dictionary(
default_regressors,
overwrite_dict=model_components.regressors,
allow_unknown_keys=False)
default_lagged_regressors = {
"lagged_regressor_dict": [None],
}
model_components.lagged_regressors = update_dictionary(
default_lagged_regressors,
overwrite_dict=model_components.lagged_regressors,
allow_unknown_keys=False)
default_uncertainty = {
"uncertainty_dict": [None],
}
model_components.uncertainty = update_dictionary(
default_uncertainty,
overwrite_dict=model_components.uncertainty,
allow_unknown_keys=False)
if time_properties is not None:
origin_for_time_vars = time_properties.get("origin_for_time_vars")
else:
origin_for_time_vars = None
default_custom = {
"silverkite":
[SilverkiteForecast()], # NB: sklearn creates a copy in grid search
"silverkite_diagnostics": [SilverkiteDiagnostics()],
# The same origin for every split, based on start year of full dataset.
# To use first date of each training split, set to `None` in model_components.
"origin_for_time_vars": [origin_for_time_vars],
"extra_pred_cols": ["ct1"], # linear growth
"fit_algorithm_dict": [{
"fit_algorithm": "linear",
"fit_algorithm_params": None,
}],
"min_admissible_value": [None],
"max_admissible_value": [None],
}
model_components.custom = update_dictionary(
default_custom,
overwrite_dict=model_components.custom,
allow_unknown_keys=False)
# sets to {} if None, for each item if
# `model_components.hyperparameter_override` is a list of dictionaries
model_components.hyperparameter_override = update_dictionaries(
{}, overwrite_dicts=model_components.hyperparameter_override)
return model_components
def plot_multivariate(df,
x_col,
y_col_style_dict="plotly",
default_color="rgba(0, 145, 202, 1.0)",
xlabel=None,
ylabel=cst.VALUE_COL,
title=None,
showlegend=True):
"""Plots one or more lines against the same x-axis values.
Parameters
----------
df : `pandas.DataFrame`
Data frame with ``x_col`` and columns named by the keys in ``y_col_style_dict``.
x_col: `str`
Which column to plot on the x-axis.
y_col_style_dict: `dict` [`str`, `dict` or None] or "plotly" or "auto" or "auto-fill", default "plotly"
The column(s) to plot on the y-axis, and how to style them.
If a dictionary:
- key : `str`
column name in ``df``
- value : `dict` or None
Optional styling options, passed as kwargs to `go.Scatter`.
If None, uses the default: line labeled by the column name.
See reference page for `plotly.graph_objs.Scatter` for options
(e.g. color, mode, width/size, opacity).
https://plotly.com/python/reference/#scatter.
If a string, plots all columns in ``df`` besides ``x_col`` against ``x_col``:
- "plotly": plot lines with default plotly styling
- "auto": plot lines with color ``default_color``, sorted by value (ascending)
- "auto-fill": plot lines with color ``default_color``, sorted by value (ascending), and fills between lines
default_color: `str`, default "rgba(0, 145, 202, 1.0)" (blue)
Default line color when ``y_col_style_dict`` is one of "auto", "auto-fill".
xlabel : `str` or None, default None
x-axis label. If None, default is ``x_col``.
ylabel : `str` or None, default ``VALUE_COL``
y-axis label
title : `str` or None, default None
Plot title. If None, default is based on axis labels.
showlegend : `bool`, default True
Whether to show the legend.
Returns
-------
fig : `plotly.graph_objs.Figure`
Interactive plotly graph of one or more columns
in ``df`` against ``x_col``.
See `~greykite.common.viz.timeseries_plotting.plot_forecast_vs_actual`
return value for how to plot the figure and add customization.
"""
if xlabel is None:
xlabel = x_col
if title is None and ylabel is not None:
title = f"{ylabel} vs {xlabel}"
auto_style = {"line": {"color": default_color}}
if y_col_style_dict == "plotly":
# Uses plotly default style
y_col_style_dict = {col: None for col in df.columns if col != x_col}
elif y_col_style_dict in ["auto", "auto-fill"]:
# Columns ordered from low to high
means = df.drop(columns=x_col).mean()
column_order = list(means.sort_values().index)
if y_col_style_dict == "auto":
# Lines with color `default_color`
y_col_style_dict = {col: auto_style for col in column_order}
elif y_col_style_dict == "auto-fill":
# Lines with color `default_color`, with fill between lines
y_col_style_dict = {column_order[0]: auto_style}
y_col_style_dict.update({
col: {
"line": {
"color": default_color
},
"fill": "tonexty"
}
for col in column_order[1:]
})
data = []
default_style = dict(mode="lines")
for column, style_dict in y_col_style_dict.items():
# By default, column name in ``df`` is used to label the line
default_col_style = update_dictionary(default_style,
overwrite_dict={"name": column})
# User can overwrite any of the default values, or remove them by setting key value to None
style_dict = update_dictionary(default_col_style,
overwrite_dict=style_dict)
line = go.Scatter(x=df[x_col], y=df[column], **style_dict)
data.append(line)
layout = go.Layout(
xaxis=dict(title=xlabel),
yaxis=dict(title=ylabel),
title=title,
showlegend=showlegend,
legend={'traceorder': 'reversed'
} # Matches the order of ``y_col_style_dict`` (bottom to top)
)
fig = go.Figure(data=data, layout=layout)
return fig
def plot_multivariate_grouped(df,
x_col,
y_col_style_dict,
grouping_x_col,
grouping_x_col_values,
grouping_y_col_style_dict,
colors=DEFAULT_PLOTLY_COLORS,
xlabel=None,
ylabel=cst.VALUE_COL,
title=None,
showlegend=True):
"""Plots multiple lines against the same x-axis values. The lines can
partially share the x-axis values.
See parameter descriptions for a running example.
Parameters
----------
df : `pandas.DataFrame`
Data frame with ``x_col`` and columns named by the keys in ``y_col_style_dict``,
``grouping_x_col``, ``grouping_y_col_style_dict``.
For example::
df = pd.DataFrame({
time: [dt(2018, 1, 1),
dt(2018, 1, 2),
dt(2018, 1, 3)],
"y1": [8.5, 2.0, 3.0],
"y2": [1.4, 2.1, 3.4],
"y3": [4.2, 3.1, 3.0],
"y4": [0, 1, 2],
"y5": [10, 9, 8],
"group": [1, 2, 1],
})
This will be our running example.
x_col: `str`
Which column to plot on the x-axis.
"time" in our example.
y_col_style_dict: `dict` [`str`, `dict` or None]
The column(s) to plot on the y-axis, and how to style them.
These columns are plotted against the complete x-axis.
- key : `str`
column name in ``df``
- value : `dict` or None
Optional styling options, passed as kwargs to `go.Scatter`.
If None, uses the default: line labeled by the column name.
If line color is not given, it is added according to ``colors``.
See reference page for `plotly.graph_objs.Scatter` for options
(e.g. color, mode, width/size, opacity).
https://plotly.com/python/reference/#scatter.
For example::
y_col_style_dict={
"y1": {
"name": "y1_name",
"legendgroup": "one",
"mode": "markers",
"line": None # Remove line params since we use mode="markers"
},
"y2": None,
}
The function will add a line color to "y1" and "y2" based on the ``colors`` parameter.
It will also add a name to "y2", since none was given. The "name" of "y1" will be preserved.
The output ``fig`` will have one line each for each of "y1" and "y2", each plot against
the entire "time" column.
grouping_x_col: `str`
Which column to use to group columns in ``grouping_y_col_style_dict``.
"group" in our example.
grouping_x_col_values: `list` [`int`] or None
Which values to use for grouping. If None, uses all the unique values in
``df`` [``grouping_x_col``].
In our example, specifying ``grouping_x_col_values == [1, 2]`` would plot
separate lines corresponding to ``group==1`` and ``group==2``.
grouping_y_col_style_dict: `dict` [`str`, `dict` or None]
The column(s) to plot on the y-axis, and how to style them.
These columns are plotted against partial x-axis.
For each ``grouping_x_col_values`` an element in this dictionary produces
one line.
- key : `str`
column name in ``df``
- value : `dict` or None
Optional styling options, passed as kwargs to `go.Scatter`.
If None, uses the default: line labeled by the ``grouping_x_col_values``,
``grouping_x_col`` and column name.
If a name is given, it is augmented with the ``grouping_x_col_values``.
If line color is not given, it is added according to ``colors``.
All the lines sharing same ``grouping_x_col_values`` have the same color.
See reference page for `plotly.graph_objs.Scatter` for options
(e.g. color, mode, width/size, opacity).
https://plotly.com/python/reference/#scatter.
For example::
grouping_y_col_style_dict={
"y3": {
"line": {
"color": "blue"
}
},
"y4": {
"name": "y4_name",
"line": {
"width": 2,
"dash": "dot"
}
},
"y5": None,
}
The function will add a line color to "y4" and "y5" based on the ``colors`` parameter.
The line color of "y3" will be "blue" as specified. We also preserve the given line
properties of "y4".
` The function adds a name to "y3" and "y5", since none was given. The given "name" of "y4"
will be augmented with ``grouping_x_col_values``.
Each element of ``grouping_y_col_style_dict`` gets one line for each ``grouping_x_col_values``.
In our example, there will be 2 lines corresponding to "y3", named "1_y3" and "2_y3".
"1_y3" is plotted against "time = [dt(2018, 1, 1), dt(2018, 1, 3)]", corresponding to ``group==1``.
"2_y3" is plotted against "time = [dt(2018, 1, 2)", corresponding to ``group==2``.
colors: [`str`, `list` [`str`]], default ``DEFAULT_PLOTLY_COLORS``
Which colors to use to build a color palette for plotting.
This can be a list of RGB colors or a `str` from ``PLOTLY_SCALES``.
Required number of colors equals sum of the length of ``y_col_style_dict``
and length of ``grouping_x_col_values``.
See `~greykite.common.viz.colors_utils.get_color_palette` for details.
xlabel : `str` or None, default None
x-axis label. If None, default is ``x_col``.
ylabel : `str` or None, default ``VALUE_COL``
y-axis label
title : `str` or None, default None
Plot title. If None, default is based on axis labels.
showlegend : `bool`, default True
Whether to show the legend.
Returns
-------
fig : `plotly.graph_objs.Figure`
Interactive plotly graph of one or more columns
in ``df`` against ``x_col``.
See `~greykite.common.viz.timeseries_plotting.plot_forecast_vs_actual`
return value for how to plot the figure and add customization.
"""
available_grouping_x_col_values = np.unique(df[grouping_x_col])
if grouping_x_col_values is None:
grouping_x_col_values = available_grouping_x_col_values
else:
missing_grouping_x_col_values = set(grouping_x_col_values) - set(
available_grouping_x_col_values)
if len(missing_grouping_x_col_values) > 0:
raise ValueError(
f"Following 'grouping_x_col_values' are missing in '{grouping_x_col}' column: "
f"{missing_grouping_x_col_values}")
# Chooses the color palette
n_color = len(y_col_style_dict) + len(grouping_x_col_values)
color_palette = get_color_palette(num=n_color, colors=colors)
# Updates colors for y_col_style_dict if it is not specified
for color_num, (column, style_dict) in enumerate(y_col_style_dict.items()):
if style_dict is None:
style_dict = {}
default_color = {"color": color_palette[color_num]}
style_dict["line"] = update_dictionary(
default_color, overwrite_dict=style_dict.get("line"))
y_col_style_dict[column] = style_dict
# Standardizes dataset for the next figure
df_standardized = df.copy().drop_duplicates(subset=[x_col]).sort_values(
by=x_col)
# This figure plots the whole xaxis vs yaxis values
fig = plot_multivariate(df=df_standardized,
x_col=x_col,
y_col_style_dict=y_col_style_dict,
xlabel=xlabel,
ylabel=ylabel,
title=title,
showlegend=showlegend)
data = fig.data
layout = fig.layout
# These figures plot the sliced xaxis vs yaxis values
for color_num, grouping_x_col_value in enumerate(grouping_x_col_values,
len(y_col_style_dict)):
default_color = {"color": color_palette[color_num]}
sliced_y_col_style_dict = grouping_y_col_style_dict.copy()
for column, style_dict in sliced_y_col_style_dict.items():
# Updates colors if it is not specified
if style_dict is None:
style_dict = {}
line_dict = update_dictionary(
default_color, overwrite_dict=style_dict.get("line"))
# Augments names with grouping_x_col_value
name = style_dict.get("name")
if name is None:
updated_name = f"{grouping_x_col_value}_{grouping_x_col}_{column}"
else:
updated_name = f"{grouping_x_col_value}_{name}"
overwrite_dict = {"name": updated_name, "line": line_dict}
style_dict = update_dictionary(style_dict,
overwrite_dict=overwrite_dict)
sliced_y_col_style_dict[column] = style_dict
df_sliced = df[df[grouping_x_col] == grouping_x_col_value]
fig = plot_multivariate(df=df_sliced,
x_col=x_col,
y_col_style_dict=sliced_y_col_style_dict)
data = data + fig.data
fig = go.Figure(data=data, layout=layout)
return fig
def fit(
self,
X,
y=None,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
**fit_params):
"""Fits ``Silverkite`` forecast model.
Parameters
----------
X: `pandas.DataFrame`
Input timeseries, with timestamp column,
value column, and any additional regressors.
The value column is the response, included in
``X`` to allow transformation by `sklearn.pipeline`.
y: ignored
The original timeseries values, ignored.
(The ``y`` for fitting is included in ``X``).
time_col: `str`
Time column name in ``X``.
value_col: `str`
Value column name in ``X``.
fit_params: `dict`
additional parameters for null model.
"""
# Initializes `fit_algorithm_dict` with default values.
# This cannot be done in __init__ to remain compatible
# with sklearn grid search.
default_fit_algorithm_dict = {
"fit_algorithm": "linear",
"fit_algorithm_params": None}
self.fit_algorithm_dict = update_dictionary(
default_fit_algorithm_dict,
overwrite_dict=self.fit_algorithm_dict)
# fits null model
super().fit(
X=X,
y=y,
time_col=time_col,
value_col=value_col,
**fit_params)
self.model_dict = self.silverkite.forecast(
df=X,
time_col=time_col,
value_col=value_col,
origin_for_time_vars=self.origin_for_time_vars,
extra_pred_cols=self.extra_pred_cols,
train_test_thresh=self.train_test_thresh,
training_fraction=self.training_fraction,
fit_algorithm=self.fit_algorithm_dict["fit_algorithm"],
fit_algorithm_params=self.fit_algorithm_dict["fit_algorithm_params"],
daily_event_df_dict=self.daily_event_df_dict,
fs_components_df=self.fs_components_df,
autoreg_dict=self.autoreg_dict,
lagged_regressor_dict=self.lagged_regressor_dict,
changepoints_dict=self.changepoints_dict,
seasonality_changepoints_dict=self.seasonality_changepoints_dict,
changepoint_detector=self.changepoint_detector,
min_admissible_value=self.min_admissible_value,
max_admissible_value=self.max_admissible_value,
uncertainty_dict=self.uncertainty_dict,
normalize_method=self.normalize_method,
adjust_anomalous_dict=self.adjust_anomalous_dict,
impute_dict=self.impute_dict,
regression_weight_col=self.regression_weight_col,
forecast_horizon=self.forecast_horizon,
simulation_based=self.simulation_based)
# sets attributes based on ``self.model_dict``
super().finish_fit()
return self