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Dr. Kashif Rasul
2019-12-08 20:37:23 +01:00
parent f99d11251b
commit a6a5c8c188
1 changed files with 81 additions and 85 deletions
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pts/model/forecast.py
+81 -85
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@@ -1,6 +1,6 @@
from abc import ABC, abstractmethod
from enum import Enum
from typing import Dict, List, Optional, Set, Union
from typing import Dict, List, Optional, Set, Union, Callable
import numpy as np
import pandas as pd
@@ -50,7 +50,7 @@ class Forecast(ABC):
@property
def median(self) -> np.ndarray:
return self.quantile(0.5)
def plot(
self,
prediction_intervals=(50.0, 90.0),
@@ -95,14 +95,13 @@ class Forecast(ABC):
assert 0.0 <= c <= 100.0
ps = [50.0] + [
50.0 + f * c / 2.0
for c in prediction_intervals
50.0 + f * c / 2.0 for c in prediction_intervals
for f in [-1.0, +1.0]
]
percentiles_sorted = sorted(set(ps))
def alpha_for_percentile(p):
return (p / 100.0) ** 0.3
return (p / 100.0)**0.3
ps_data = [self.quantile(p / 100.0) for p in percentiles_sorted]
i_p50 = len(percentiles_sorted) // 2
@@ -150,44 +149,11 @@ class Forecast(ABC):
@property
def index(self) -> pd.DatetimeIndex:
if self._index is None:
self._index = pd.date_range(
self.start_date, periods=self.prediction_length, freq=self.freq
)
self._index = pd.date_range(self.start_date,
periods=self.prediction_length,
freq=self.freq)
return self._index
# @abstractmethod
# def dim(self) -> int:
# """
# Returns the dimensionality of the forecast object.
# """
# pass
# @abstractmethod
# def copy_dim(self, dim: int):
# """
# Returns a new Forecast object with only the selected sub-dimension.
# Parameters
# ----------
# dim
# The returned forecast object will only represent this dimension.
# """
# pass
# @abstractmethod
# def copy_aggregate(self, agg_fun: Callable):
# """
# Returns a new Forecast object with a time series aggregated over the
# dimension axis.
# Parameters
# ----------
# agg_fun
# Aggregation function that defines the aggregation operation
# (typically mean or sum).
# """
# pass
def as_json_dict(self, config: "Config") -> dict:
result = {}
@@ -225,7 +191,6 @@ class SampleForecast(Forecast):
additional information that the forecaster may provide e.g. estimated
parameters, number of iterations ran etc.
"""
def __init__(
self,
samples: Union[torch.Tensor, np.ndarray],
@@ -235,14 +200,15 @@ class SampleForecast(Forecast):
info: Optional[Dict] = None,
):
assert isinstance(
samples, (np.ndarray, torch.Tensor)
), "samples should be either a numpy array or an torch tensor"
samples,
(np.ndarray, torch.Tensor
)), "samples should be either a numpy array or an torch tensor"
assert (
len(np.shape(samples)) == 2 or len(np.shape(samples)) == 3
), "samples should be a 2-dimensional or 3-dimensional array. Dimensions found: {}".format(
len(np.shape(samples))
)
self.samples = samples if (isinstance(samples, np.ndarray)) else samples.numpy()
len(np.shape(samples)))
self.samples = samples if (isinstance(
samples, np.ndarray)) else samples.numpy()
self._sorted_samples_value = None
self._mean = None
self._dim = None
@@ -250,8 +216,8 @@ class SampleForecast(Forecast):
self.info = info
assert isinstance(
start_date, pd.Timestamp
), "start_date should be a pandas Timestamp object"
start_date,
pd.Timestamp), "start_date should be a pandas Timestamp object"
self.start_date = start_date
assert isinstance(freq, str), "freq should be a string"
@@ -300,14 +266,21 @@ class SampleForecast(Forecast):
return self._sorted_samples[sample_idx, :]
def copy_dim(self, dim: int):
"""
Returns a new Forecast object with only the selected sub-dimension.
Parameters
----------
dim
The returned forecast object will only represent this dimension.
"""
if len(self.samples.shape) == 2:
samples = self.samples
else:
target_dim = self.samples.shape[2]
assert dim < target_dim, (
f"must set 0 <= dim < target_dim, but got dim={dim},"
f" target_dim={target_dim}"
)
f" target_dim={target_dim}")
samples = self.samples[:, :, dim]
return SampleForecast(
@@ -318,7 +291,34 @@ class SampleForecast(Forecast):
info=self.info,
)
def copy_aggregate(self, agg_fun: Callable):
"""
Returns a new Forecast object with a time series aggregated over the
dimension axis.
Parameters
----------
agg_fun
Aggregation function that defines the aggregation operation
(typically mean or sum).
"""
if len(self.samples.shape) == 2:
samples = self.samples
else:
# Aggregate over target dimension axis
samples = agg_fun(self.samples, axis=2)
return SampleForecast(
samples=samples,
start_date=self.start_date,
freq=self.freq,
item_id=self.item_id,
info=self.info,
)
def dim(self) -> int:
"""
Returns the dimensionality of the forecast object.
"""
if self._dim is not None:
return self._dim
else:
@@ -340,15 +340,13 @@ class SampleForecast(Forecast):
return result
def __repr__(self):
return ", ".join(
[
f"SampleForecast({self.samples!r})",
f"{self.start_date!r}",
f"{self.freq!r}",
f"item_id={self.item_id!r}",
f"info={self.info!r})",
]
)
return ", ".join([
f"SampleForecast({self.samples!r})",
f"{self.start_date!r}",
f"{self.freq!r}",
f"item_id={self.item_id!r}",
f"info={self.info!r})",
])
class QuantileForecast(Forecast):
@@ -371,7 +369,6 @@ class QuantileForecast(Forecast):
additional information that the forecaster may provide e.g. estimated
parameters, number of iterations ran etc.
"""
def __init__(
self,
forecast_arrays: np.ndarray,
@@ -397,11 +394,11 @@ class QuantileForecast(Forecast):
shape = self.forecast_array.shape
assert shape[0] == len(self.forecast_keys), (
f"The forecast_array (shape={shape} should have the same "
f"length as the forecast_keys (len={len(self.forecast_keys)})."
)
f"length as the forecast_keys (len={len(self.forecast_keys)}).")
self.prediction_length = shape[-1]
self._forecast_dict = {
k: self.forecast_array[i] for i, k in enumerate(self.forecast_keys)
k: self.forecast_array[i]
for i, k in enumerate(self.forecast_keys)
}
self._nan_out = np.array([np.nan] * self.prediction_length)
@@ -419,29 +416,28 @@ class QuantileForecast(Forecast):
return self._forecast_dict.get("mean", self._nan_out)
def dim(self) -> int:
"""
Returns the dimensionality of the forecast object.
"""
if self._dim is not None:
return self._dim
else:
if (
len(self.forecast_array.shape) == 2
): # 1D target. shape: (num_samples, prediction_length)
if (len(self.forecast_array.shape) == 2
): # 1D target. shape: (num_samples, prediction_length)
return 1
else:
return self.forecast_array.shape[
1
] # 2D target. shape: (num_samples, target_dim, prediction_length)
1] # 2D target. shape: (num_samples, target_dim, prediction_length)
def __repr__(self):
return ", ".join(
[
f"QuantileForecast({self.forecast_array!r})",
f"start_date={self.start_date!r}",
f"freq={self.freq!r}",
f"forecast_keys={self.forecast_keys!r}",
f"item_id={self.item_id!r}",
f"info={self.info!r})",
]
)
return ", ".join([
f"QuantileForecast({self.forecast_array!r})",
f"start_date={self.start_date!r}",
f"freq={self.freq!r}",
f"forecast_keys={self.forecast_keys!r}",
f"item_id={self.item_id!r}",
f"info={self.info!r})",
])
class DistributionForecast(Forecast):
@@ -470,12 +466,12 @@ class DistributionForecast(Forecast):
parameters, number of iterations ran etc.
"""
def __init__(
self,
distribution: Distribution,
start_date,
freq,
item_id: Optional[str] = None,
info: Optional[Dict] = None,
self,
distribution: Distribution,
start_date,
freq,
item_id: Optional[str] = None,
info: Optional[Dict] = None,
):
self.distribution = distribution
self.shape = (self.distribution.batch_shape +