Feature/accuracy #73
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Feature/accuracy #73
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Codecov Report
@@ Coverage Diff @@
## master #73 +/- ##
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+ Coverage 92.6% 92.85% +0.25%
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Files 7 7
Lines 500 532 +32
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+ Hits 463 494 +31
- Misses 37 38 +1
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Rambatino
commented
Jul 7, 2017
| @@ -111,13 +113,14 @@ def from_pandas_df(df, i_variables, d_variable, alpha_merge=0.05, max_depth=2, | |||
| the type of dependent variable. Supported variable types are 'categorical' or | |||
| 'continuous' | |||
| """ | |||
| ind_df = df[list(i_variables.keys())] | |||
| df_ordered_keys = [x for x in df.columns if x in i_variables.keys()] | |||
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This is kinda gross. Will need to discuss this with you @xulaus, it's logic that seems to be the best in a given situation, but isn't foolproof. Actually, I don't think this can every go into prod.
Rambatino
commented
Jul 7, 2017
| ind_values = ind_df.values | ||
| dep_values = df[d_variable].values | ||
| weights = df[weight] if weight is not None else None | ||
| return Tree(ind_values, dep_values, alpha_merge, max_depth, min_parent_node_size, | ||
| min_child_node_size, list(ind_df.columns.values), split_threshold, weights, | ||
| list(i_variables.values()), dep_variable_type) | ||
| [i_variables[key] for key in df_ordered_keys], dep_variable_type) |
Rambatino
commented
Jul 7, 2017
| else: | ||
| tree_predictions = tree_predictions.append(pd.DataFrame([[node.node_id, node.predict]] * len(index), index=index)) | ||
| tree_predictions.columns = ['node_id', 'prediction'] | ||
| # need to retroactively fill missing values |
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dataset may not contain all the other i variables at that node, and thus they will be missed off of the node
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This adds an accuracy method to Tree.
fixes #72