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<article id="content">
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<header>
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<h1 class="title">Module <code>lang_main.analysis.preprocessing</code></h1>
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</header>
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<section id="section-intro">
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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<dt id="lang_main.analysis.preprocessing.analyse_feature"><code class="name flex">
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<span>def <span class="ident">analyse_feature</span></span>(<span>data: DataFrame, target_feature: str) ‑> tuple[pandas.core.frame.DataFrame]</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def analyse_feature(
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data: DataFrame,
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target_feature: str,
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) -> tuple[DataFrame]:
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# feature columns
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feature_entries = data[target_feature]
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logger.info(
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'Number of entries for feature >>%s<<: %d', target_feature, len(feature_entries)
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)
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# obtain unique entries
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unique_feature_entries = feature_entries.unique()
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# prepare result DataFrame
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cols = ['batched_idxs', 'entry', 'len', 'num_occur', 'assoc_obj_ids', 'num_assoc_obj_ids']
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result_df = pd.DataFrame(columns=cols)
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for entry in tqdm(unique_feature_entries, mininterval=1.0):
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len_entry = len(entry)
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filt = data[target_feature] == entry
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temp = data[filt]
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batched_idxs = temp.index.to_numpy()
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assoc_obj_ids = temp['ObjektID'].unique()
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assoc_obj_ids = np.sort(assoc_obj_ids, kind='stable')
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num_assoc_obj_ids = len(assoc_obj_ids)
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num_dupl = filt.sum()
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conc_df = pd.DataFrame(
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data=[
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[batched_idxs, entry, len_entry, num_dupl, assoc_obj_ids, num_assoc_obj_ids]
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],
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columns=cols,
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)
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result_df = pd.concat([result_df, conc_df], ignore_index=True)
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result_df = result_df.sort_values(
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by=['num_occur', 'len'], ascending=[False, False]
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).copy()
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return (result_df,)</code></pre>
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</details>
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<div class="desc"></div>
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</dd>
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<dt id="lang_main.analysis.preprocessing.load_raw_data"><code class="name flex">
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<span>def <span class="ident">load_raw_data</span></span>(<span>path: Path,<br>date_cols: Collection[str] = ('VorgangsDatum', 'ErledigungsDatum', 'Arbeitsbeginn', 'ErstellungsDatum')) ‑> tuple[pandas.core.frame.DataFrame]</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def load_raw_data(
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path: Path,
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date_cols: Collection[str] = (
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'VorgangsDatum',
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'ErledigungsDatum',
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'Arbeitsbeginn',
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'ErstellungsDatum',
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),
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) -> tuple[DataFrame]:
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"""load IHM dataset with standard structure
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Parameters
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----------
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path : str
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path to dataset file, usually CSV file
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date_cols : Collection[str], optional
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columns which contain dates and are parsed as such,
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by default (
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'VorgangsDatum',
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'ErledigungsDatum',
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'Arbeitsbeginn',
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'ErstellungsDatum',
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)
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Returns
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-------
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DataFrame
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raw dataset as DataFrame
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"""
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# load dataset
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date_cols = list(date_cols)
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data = pd.read_csv(
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filepath_or_buffer=path,
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sep=';',
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encoding='cp1252',
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parse_dates=list(date_cols),
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dayfirst=True,
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)
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logger.info('Loaded dataset successfully.')
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logger.info(
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(
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f'Dataset properties: number of entries: {len(data)}, '
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f'number of features {len(data.columns)}'
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)
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)
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return (data,)</code></pre>
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</details>
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<div class="desc"><p>load IHM dataset with standard structure</p>
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<h2 id="parameters">Parameters</h2>
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<dl>
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<dt><strong><code>path</code></strong> : <code>str</code></dt>
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<dd>path to dataset file, usually CSV file</dd>
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<dt><strong><code>date_cols</code></strong> : <code>Collection[str]</code>, optional</dt>
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<dd>columns which contain dates and are parsed as such,
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by default (
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'VorgangsDatum',
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'ErledigungsDatum',
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'Arbeitsbeginn',
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'ErstellungsDatum',
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)</dd>
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</dl>
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<h2 id="returns">Returns</h2>
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<dl>
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<dt><code>DataFrame</code></dt>
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<dd>raw dataset as DataFrame</dd>
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</dl></div>
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</dd>
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<dt id="lang_main.analysis.preprocessing.merge_similarity_duplicates"><code class="name flex">
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<span>def <span class="ident">merge_similarity_duplicates</span></span>(<span>data: DataFrame, model: SentenceTransformer, cos_sim_threshold: float) ‑> tuple[pandas.core.frame.DataFrame]</span>
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||
</code></dt>
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||
<dd>
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||
<details class="source">
|
||
<summary>
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||
<span>Expand source code</span>
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||
</summary>
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||
<pre><code class="python">def merge_similarity_duplicates(
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data: DataFrame,
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model: SentenceTransformer,
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cos_sim_threshold: float,
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) -> tuple[DataFrame]:
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logger.info('Start merging of similarity candidates...')
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# data
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merged_data = data.copy()
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model_input = merged_data['entry']
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candidates_idx = candidates_by_index(
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data_model_input=model_input,
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model=model,
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cos_sim_threshold=cos_sim_threshold,
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)
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# graph of similar ids
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similar_id_graph, _ = similar_index_connection_graph(candidates_idx)
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for similar_id_group in similar_index_groups(similar_id_graph):
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similar_id_group = list(similar_id_group)
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similar_data = merged_data.loc[similar_id_group, :]
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# keep first entry with max number occurrences, then number of
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# associated objects, then length of entry
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similar_data = similar_data.sort_values(
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by=['num_occur', 'num_assoc_obj_ids', 'len'],
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ascending=[False, False, False],
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)
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# merge information to first entry
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data_idx = cast(PandasIndex, similar_data.index[0])
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similar_data.at[data_idx, 'num_occur'] = similar_data['num_occur'].sum()
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assoc_obj_ids = similar_data['assoc_obj_ids'].to_numpy()
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assoc_obj_ids = np.concatenate(assoc_obj_ids)
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assoc_obj_ids = np.unique(assoc_obj_ids)
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similar_data.at[data_idx, 'assoc_obj_ids'] = assoc_obj_ids
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similar_data.at[data_idx, 'num_assoc_obj_ids'] = len(assoc_obj_ids)
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# remaining indices, should be removed
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similar_id_group.remove(data_idx)
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merged_similar_data = similar_data.drop(index=similar_id_group)
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# update entry in main dataset, drop remaining entries
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merged_data.update(merged_similar_data)
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merged_data = merged_data.drop(index=similar_id_group)
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logger.info('Similarity candidates merged successfully.')
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return (merged_data,)</code></pre>
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</details>
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<div class="desc"></div>
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</dd>
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<dt id="lang_main.analysis.preprocessing.numeric_pre_filter_feature"><code class="name flex">
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||
<span>def <span class="ident">numeric_pre_filter_feature</span></span>(<span>data: DataFrame, feature: str, bound_lower: int | None, bound_upper: int | None) ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
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||
</summary>
|
||
<pre><code class="python">def numeric_pre_filter_feature(
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data: DataFrame,
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feature: str,
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bound_lower: int | None,
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||
bound_upper: int | None,
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) -> tuple[DataFrame]:
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||
"""filter DataFrame for a given numerical feature regarding their bounds
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bounds are inclusive: entries (bound_lower <= entry <= bound_upper) are retained
|
||
|
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Parameters
|
||
----------
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||
data : DataFrame
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||
DataFrame to filter
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||
feature : str
|
||
feature name to filter
|
||
bound_lower : int | None
|
||
lower bound of values to retain
|
||
bound_upper : int | None
|
||
upper bound of values to retain
|
||
|
||
Returns
|
||
-------
|
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tuple[DataFrame]
|
||
filtered DataFrame
|
||
|
||
Raises
|
||
------
|
||
ValueError
|
||
if no bounds are provided, at least one bound must be set
|
||
"""
|
||
if not any([bound_lower, bound_upper]):
|
||
raise ValueError('No bounds for filtering provided')
|
||
|
||
data = data.copy()
|
||
if bound_lower is None:
|
||
bound_lower = cast(int, data[feature].min())
|
||
if bound_upper is None:
|
||
bound_upper = cast(int, data[feature].max())
|
||
|
||
filter_lower = data[feature] >= bound_lower
|
||
filter_upper = data[feature] <= bound_upper
|
||
filter = filter_lower & filter_upper
|
||
|
||
data = data.loc[filter]
|
||
|
||
return (data,)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>filter DataFrame for a given numerical feature regarding their bounds
|
||
bounds are inclusive: entries (bound_lower <= entry <= bound_upper) are retained</p>
|
||
<h2 id="parameters">Parameters</h2>
|
||
<dl>
|
||
<dt><strong><code>data</code></strong> : <code>DataFrame</code></dt>
|
||
<dd>DataFrame to filter</dd>
|
||
<dt><strong><code>feature</code></strong> : <code>str</code></dt>
|
||
<dd>feature name to filter</dd>
|
||
<dt><strong><code>bound_lower</code></strong> : <code>int | None</code></dt>
|
||
<dd>lower bound of values to retain</dd>
|
||
<dt><strong><code>bound_upper</code></strong> : <code>int | None</code></dt>
|
||
<dd>upper bound of values to retain</dd>
|
||
</dl>
|
||
<h2 id="returns">Returns</h2>
|
||
<dl>
|
||
<dt><code>tuple[DataFrame]</code></dt>
|
||
<dd>filtered DataFrame</dd>
|
||
</dl>
|
||
<h2 id="raises">Raises</h2>
|
||
<dl>
|
||
<dt><code>ValueError</code></dt>
|
||
<dd>if no bounds are provided, at least one bound must be set</dd>
|
||
</dl></div>
|
||
</dd>
|
||
<dt id="lang_main.analysis.preprocessing.remove_NA"><code class="name flex">
|
||
<span>def <span class="ident">remove_NA</span></span>(<span>data: DataFrame, target_features: Collection[str] = ('VorgangsBeschreibung',)) ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def remove_NA(
|
||
data: DataFrame,
|
||
target_features: Collection[str] = ('VorgangsBeschreibung',),
|
||
) -> tuple[DataFrame]:
|
||
"""function to drop NA entries based on a subset of features to be analysed
|
||
|
||
Parameters
|
||
----------
|
||
data : DataFrame
|
||
standard IHM dataset, perhaps pre-cleaned
|
||
target_features : Collection[str], optional
|
||
subset to analyse to define an NA entry, by default ('VorgangsBeschreibung',)
|
||
|
||
Returns
|
||
-------
|
||
DataFrame
|
||
dataset with removed NA entries for given subset of features
|
||
"""
|
||
target_features = list(target_features)
|
||
wo_NA = data.dropna(axis=0, subset=target_features, ignore_index=True).copy() # type: ignore
|
||
logger.info(
|
||
f'Removed NA entries for features >>{target_features}<< from dataset successfully.'
|
||
)
|
||
|
||
return (wo_NA,)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>function to drop NA entries based on a subset of features to be analysed</p>
|
||
<h2 id="parameters">Parameters</h2>
|
||
<dl>
|
||
<dt><strong><code>data</code></strong> : <code>DataFrame</code></dt>
|
||
<dd>standard IHM dataset, perhaps pre-cleaned</dd>
|
||
<dt><strong><code>target_features</code></strong> : <code>Collection[str]</code>, optional</dt>
|
||
<dd>subset to analyse to define an NA entry, by default ('VorgangsBeschreibung',)</dd>
|
||
</dl>
|
||
<h2 id="returns">Returns</h2>
|
||
<dl>
|
||
<dt><code>DataFrame</code></dt>
|
||
<dd>dataset with removed NA entries for given subset of features</dd>
|
||
</dl></div>
|
||
</dd>
|
||
<dt id="lang_main.analysis.preprocessing.remove_duplicates"><code class="name flex">
|
||
<span>def <span class="ident">remove_duplicates</span></span>(<span>data: DataFrame) ‑> tuple[pandas.core.frame.DataFrame]</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def remove_duplicates(
|
||
data: DataFrame,
|
||
) -> tuple[DataFrame]:
|
||
"""removes duplicated entries over all features in the given dataset
|
||
|
||
Parameters
|
||
----------
|
||
data : DataFrame
|
||
read data with standard structure
|
||
|
||
Returns
|
||
-------
|
||
DataFrame
|
||
dataset with removed duplicates over all features
|
||
"""
|
||
# obtain info about duplicates over all features
|
||
duplicates_filt = data.duplicated()
|
||
logger.info(f'Number of duplicates over all features: {duplicates_filt.sum()}')
|
||
# drop duplicates
|
||
wo_duplicates = data.drop_duplicates(ignore_index=True)
|
||
duplicates_subset: list[str] = [
|
||
'VorgangsID',
|
||
'ObjektID',
|
||
]
|
||
duplicates_subset_filt = wo_duplicates.duplicated(subset=duplicates_subset)
|
||
logger.info(
|
||
(
|
||
'Number of duplicates over subset '
|
||
f'>>{duplicates_subset}<<: {duplicates_subset_filt.sum()}'
|
||
)
|
||
)
|
||
wo_duplicates = wo_duplicates.drop_duplicates(
|
||
subset=duplicates_subset, ignore_index=True
|
||
).copy()
|
||
logger.info('Removed all duplicates from dataset successfully.')
|
||
logger.info(
|
||
'New Dataset properties: number of entries: %d, number of features %d',
|
||
len(wo_duplicates),
|
||
len(wo_duplicates.columns),
|
||
)
|
||
|
||
return (wo_duplicates,)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>removes duplicated entries over all features in the given dataset</p>
|
||
<h2 id="parameters">Parameters</h2>
|
||
<dl>
|
||
<dt><strong><code>data</code></strong> : <code>DataFrame</code></dt>
|
||
<dd>read data with standard structure</dd>
|
||
</dl>
|
||
<h2 id="returns">Returns</h2>
|
||
<dl>
|
||
<dt><code>DataFrame</code></dt>
|
||
<dd>dataset with removed duplicates over all features</dd>
|
||
</dl></div>
|
||
</dd>
|
||
</dl>
|
||
</section>
|
||
<section>
|
||
</section>
|
||
</article>
|
||
<nav id="sidebar">
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||
<div class="toc">
|
||
<ul></ul>
|
||
</div>
|
||
<ul id="index">
|
||
<li><h3>Super-module</h3>
|
||
<ul>
|
||
<li><code><a title="lang_main.analysis" href="index.html">lang_main.analysis</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||
<ul class="">
|
||
<li><code><a title="lang_main.analysis.preprocessing.analyse_feature" href="#lang_main.analysis.preprocessing.analyse_feature">analyse_feature</a></code></li>
|
||
<li><code><a title="lang_main.analysis.preprocessing.load_raw_data" href="#lang_main.analysis.preprocessing.load_raw_data">load_raw_data</a></code></li>
|
||
<li><code><a title="lang_main.analysis.preprocessing.merge_similarity_duplicates" href="#lang_main.analysis.preprocessing.merge_similarity_duplicates">merge_similarity_duplicates</a></code></li>
|
||
<li><code><a title="lang_main.analysis.preprocessing.numeric_pre_filter_feature" href="#lang_main.analysis.preprocessing.numeric_pre_filter_feature">numeric_pre_filter_feature</a></code></li>
|
||
<li><code><a title="lang_main.analysis.preprocessing.remove_NA" href="#lang_main.analysis.preprocessing.remove_NA">remove_NA</a></code></li>
|
||
<li><code><a title="lang_main.analysis.preprocessing.remove_duplicates" href="#lang_main.analysis.preprocessing.remove_duplicates">remove_duplicates</a></code></li>
|
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</ul>
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</li>
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</ul>
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