Deprecate min_segments parameter

wise_pizza/explain.py

@@ -27,7 +27,7 @@ def explain_changes_in_average(
  total_name: str,
  size_name: str,
  min_segments: Optional[int] = None,
- max_segments: int = 5,
+ max_segments: int = None,
  min_depth: int = 1,
  max_depth: int = 2,
  solver: str = "lasso",
@@ -124,7 +124,7 @@ def explain_changes_in_totals(
  total_name: str,
  size_name: str,
  min_segments: Optional[int] = None,
- max_segments: int = 5,
+ max_segments: int = None,
  min_depth: int = 1,
  max_depth: int = 2,
  solver: str = "lasso",
@@ -271,7 +271,7 @@ def explain_levels(
  total_name: str,
  size_name: Optional[str] = None,
  min_segments: int = None,
- max_segments: int = 10,
+ max_segments: int = None,
  min_depth: int = 1,
  max_depth: int = 2,
  solver="lasso",
@@ -353,7 +353,7 @@ def explain_timeseries(
  time_name: str,
  size_name: Optional[str] = None,
  min_segments: int = None,
- max_segments: int = 5,
+ max_segments: int = None,
  min_depth: int = 1,
  max_depth: int = 2,
  solver: str = "omp",

wise_pizza/slicer.py

@@ -8,7 +8,8 @@
 import pandas as pd
 from scipy.sparse import csc_matrix, diags
 
-from wise_pizza.solve.find_alpha import clean_up_min_max, find_alpha
+from wise_pizza.solve.find_alpha import find_alpha
+from wise_pizza.utils import clean_up_min_max
 from wise_pizza.make_matrix import sparse_dummy_matrix
 from wise_pizza.cluster import make_clusters
 from wise_pizza.preselect import HeuristicSelector
@@ -98,7 +99,7 @@ def fit(
  weights: pd.Series = None,
  time_col: pd.Series = None,
  time_basis: pd.DataFrame = None,
- min_segments: int = 10,
+ min_segments: int = None,
  max_segments: int = None,
  min_depth: int = 1,
  max_depth: int = 3,

wise_pizza/solve/find_alpha.py

@@ -4,6 +4,7 @@
 from scipy.linalg import svd
 
 from wise_pizza.solve.solver import solve_lasso, solve_lp, solve_omp
+from wise_pizza.utils import clean_up_min_max
 
 
 def find_alpha(
@@ -219,19 +220,3 @@ def print_errors(a: np.ndarray):
  # fit_intercept=not use_proj
  )
  return reg, nonzeros
-
-
-def clean_up_min_max(min_nonzeros: int = None, max_nonzeros: int = None):
- assert min_nonzeros is not None or max_nonzeros is not None
- if max_nonzeros is None:
- if min_nonzeros is None:
- max_nonzeros = 5
- min_nonzeros = 5
- else:
- max_nonzeros = min_nonzeros
- else:
- if min_nonzeros is None:
- min_nonzeros = max_nonzeros
-
- assert min_nonzeros <= max_nonzeros
- return min_nonzeros, max_nonzeros

wise_pizza/utils.py

@@ -1,3 +1,4 @@
+import logging
 from typing import List, Optional
 
 import numpy as np
@@ -120,7 +121,9 @@ def rel_error(x, y):
 
  if return_multiple:
  sd_size = SegmentData(
- combined.rename(columns={"Change in totals": "Change from segment size"}),
+ combined.rename(
+ columns={"Change in totals": "Change from segment size"}
+ ),
  dimensions=dims,
  segment_total="Change from segment size",
  segment_size=weights,
@@ -137,7 +140,9 @@ def rel_error(x, y):
  combined["Change from"] = "Segment size"
  c2["Change from"] = "Segment average"
 
- df = pd.concat([combined, c2])[dims + [weights, "Change in totals", "Change from"]]
+ df = pd.concat([combined, c2])[
+ dims + [weights, "Change in totals", "Change from"]
+ ]
  df_change_in_totals = np.array(df["Change in totals"], dtype=np.longdouble)
  combined_dtotals = np.array(combined["dtotals"], dtype=np.longdouble)
  df_change_in_totals_sum = np.nansum(df_change_in_totals)
@@ -160,7 +165,9 @@ def rel_error(x, y):
  combined[weights] = 1.0 # combined[totals + "_x"]
  combined[weights] = np.maximum(1.0, combined[weights])
  cols = (
- dims + ["Change in totals", totals + "_x", totals + "_y"] + [c for c in combined.columns if "baseline" in c]
+ dims
+ + ["Change in totals", totals + "_x", totals + "_y"]
+ + [c for c in combined.columns if "baseline" in c]
  )
 
  return SegmentData(
@@ -205,16 +212,26 @@ def prepare_df(
 
  # replace NaN values in categorical columns with the column name + "_unknown"
  object_columns = list(new_df[dims].select_dtypes("object").columns)
- new_df[object_columns] = new_df[object_columns].fillna(new_df[object_columns].apply(lambda x: x.name + "_unknown"))
+ new_df[object_columns] = new_df[object_columns].fillna(
+ new_df[object_columns].apply(lambda x: x.name + "_unknown")
+ )
  new_df[object_columns] = new_df[object_columns].astype(str)
 
  # Groupby all relevant dims to decrease the dataframe size, if possible
  group_dims = dims if time_name is None else dims + [time_name]
 
  if size_name is not None:
- new_df = new_df.groupby(by=group_dims, observed=True)[[total_name, size_name]].sum().reset_index()
+ new_df = (
+ new_df.groupby(by=group_dims, observed=True)[[total_name, size_name]]
+ .sum()
+ .reset_index()
+ )
  else:
- new_df = new_df.groupby(by=group_dims, observed=True)[[total_name]].sum().reset_index()
+ new_df = (
+ new_df.groupby(by=group_dims, observed=True)[[total_name]]
+ .sum()
+ .reset_index()
+ )
 
  return new_df
 
@@ -280,5 +297,24 @@ def prepare_df(
 # new_df[object_columns] = new_df[object_columns].astype(str)
 #
 # return new_df
-def almost_equals(x1, x2, eps: float=1e-6) -> bool:
- return np.sum(np.abs(x1-x2))/np.mean(np.abs(x1+x2)) < eps
+def almost_equals(x1, x2, eps: float = 1e-6) -> bool:
+ return np.sum(np.abs(x1 - x2)) / np.mean(np.abs(x1 + x2)) < eps
+
+
+def clean_up_min_max(min_nonzeros: int = None, max_nonzeros: int = None):
+ if min_nonzeros is not None:
+ logging.warning(
+ "min_segments parameter is deprecated, please use max_nonzeros instead."
+ )
+ if max_nonzeros is None:
+ if min_nonzeros is None:
+ max_nonzeros = 5
+ min_nonzeros = 5
+ else:
+ max_nonzeros = min_nonzeros
+ else:
+ if min_nonzeros is None:
+ min_nonzeros = max_nonzeros
+
+ assert min_nonzeros <= max_nonzeros
+ return min_nonzeros, max_nonzeros