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<h1>Source code for pyspark.pandas.mlflow</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">MLflow-related functions to load models and apply them to pandas-on-Spark dataframes.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="n">DataType</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
<span class="kn">from</span> <span class="nn">pyspark.pandas._typing</span> <span class="kn">import</span> <span class="n">Label</span><span class="p">,</span> <span class="n">Dtype</span>
<span class="kn">from</span> <span class="nn">pyspark.pandas.utils</span> <span class="kn">import</span> <span class="n">lazy_property</span><span class="p">,</span> <span class="n">default_session</span>
<span class="kn">from</span> <span class="nn">pyspark.pandas.frame</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span> <span class="nn">pyspark.pandas.series</span> <span class="kn">import</span> <span class="n">Series</span><span class="p">,</span> <span class="n">first_series</span>
<span class="kn">from</span> <span class="nn">pyspark.pandas.typedef</span> <span class="kn">import</span> <span class="n">as_spark_type</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;PythonModelWrapper&quot;</span><span class="p">,</span> <span class="s2">&quot;load_model&quot;</span><span class="p">]</span>
<div class="viewcode-block" id="PythonModelWrapper"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.mlflow.PythonModelWrapper.html#pyspark.pandas.mlflow.PythonModelWrapper">[docs]</a><span class="k">class</span> <span class="nc">PythonModelWrapper</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A wrapper around MLflow&#39;s Python object model.</span>
<span class="sd"> This wrapper acts as a predictor on pandas-on-Spark</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_uri</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">return_type_hint</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">Dtype</span><span class="p">]):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_model_uri</span> <span class="o">=</span> <span class="n">model_uri</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_return_type_hint</span> <span class="o">=</span> <span class="n">return_type_hint</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">_return_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataType</span><span class="p">:</span>
<span class="n">hint</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_return_type_hint</span>
<span class="c1"># The logic is simple for now, because it corresponds to the default</span>
<span class="c1"># case: continuous predictions</span>
<span class="c1"># TODO: do something smarter, for example when there is a sklearn.Classifier (it should</span>
<span class="c1"># return an integer or a categorical)</span>
<span class="c1"># We can do the same for pytorch/tensorflow/keras models by looking at the output types.</span>
<span class="c1"># However, this is probably better done in mlflow than here.</span>
<span class="k">if</span> <span class="n">hint</span> <span class="o">==</span> <span class="s2">&quot;infer&quot;</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">hint</span><span class="p">:</span>
<span class="n">hint</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span>
<span class="k">return</span> <span class="n">as_spark_type</span><span class="p">(</span><span class="n">hint</span><span class="p">)</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">_model</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The return object has to follow the API of mlflow.pyfunc.PythonModel.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">mlflow</span> <span class="kn">import</span> <span class="n">pyfunc</span>
<span class="k">return</span> <span class="n">pyfunc</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">model_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_model_uri</span><span class="p">)</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">_model_udf</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">mlflow</span> <span class="kn">import</span> <span class="n">pyfunc</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">default_session</span><span class="p">()</span>
<span class="k">return</span> <span class="n">pyfunc</span><span class="o">.</span><span class="n">spark_udf</span><span class="p">(</span><span class="n">spark</span><span class="p">,</span> <span class="n">model_uri</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_model_uri</span><span class="p">,</span> <span class="n">result_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_return_type</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;PythonModelWrapper(</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="p">))</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;PythonModelWrapper(</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">Series</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a prediction on the data.</span>
<span class="sd"> If the data is a pandas-on-Spark DataFrame, the return is a pandas-on-Spark Series.</span>
<span class="sd"> If the data is a pandas Dataframe, the return is the expected output of the underlying</span>
<span class="sd"> pyfunc object (typically a pandas Series or a numpy array).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">DataFrame</span><span class="p">):</span>
<span class="n">return_col</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_udf</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">data_spark_columns</span><span class="p">)</span>
<span class="c1"># TODO: the columns should be named according to the mlflow spec</span>
<span class="c1"># However, this is only possible with spark &gt;= 3.0</span>
<span class="c1"># s = F.struct(*data.columns)</span>
<span class="c1"># return_col = self._model_udf(s)</span>
<span class="n">column_labels</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="n">col</span><span class="p">,)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">spark_frame</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">return_col</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span>
<span class="p">]</span>
<span class="n">internal</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">_internal</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span>
<span class="n">column_labels</span><span class="o">=</span><span class="n">column_labels</span><span class="p">,</span> <span class="n">data_spark_columns</span><span class="o">=</span><span class="p">[</span><span class="n">return_col</span><span class="p">],</span> <span class="n">data_fields</span><span class="o">=</span><span class="kc">None</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">first_series</span><span class="p">(</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">internal</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;unknown data type: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span></div>
<div class="viewcode-block" id="load_model"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.mlflow.load_model.html#pyspark.pandas.mlflow.load_model">[docs]</a><span class="k">def</span> <span class="nf">load_model</span><span class="p">(</span>
<span class="n">model_uri</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">predict_type</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">Dtype</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;infer&quot;</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PythonModelWrapper</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads an MLflow model into a wrapper that can be used both for pandas and pandas-on-Spark</span>
<span class="sd"> DataFrame.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> model_uri : str</span>
<span class="sd"> URI pointing to the model. See MLflow documentation for more details.</span>
<span class="sd"> predict_type : a python basic type, a numpy basic type, a Spark type or &#39;infer&#39;.</span>
<span class="sd"> This is the return type that is expected when calling the predict function of the model.</span>
<span class="sd"> If &#39;infer&#39; is specified, the wrapper will attempt to automatically determine the return type</span>
<span class="sd"> based on the model type.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> PythonModelWrapper</span>
<span class="sd"> A wrapper around MLflow PythonModel objects. This wrapper is expected to adhere to the</span>
<span class="sd"> interface of mlflow.pyfunc.PythonModel.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> Here is a full example that creates a model with scikit-learn and saves the model with</span>
<span class="sd"> MLflow. The model is then loaded as a predictor that can be applied on a pandas-on-Spark</span>
<span class="sd"> Dataframe.</span>
<span class="sd"> We first initialize our MLflow environment:</span>
<span class="sd"> &gt;&gt;&gt; from mlflow.tracking import MlflowClient, set_tracking_uri</span>
<span class="sd"> &gt;&gt;&gt; import mlflow.sklearn</span>
<span class="sd"> &gt;&gt;&gt; from tempfile import mkdtemp</span>
<span class="sd"> &gt;&gt;&gt; d = mkdtemp(&quot;pandas_on_spark_mlflow&quot;)</span>
<span class="sd"> &gt;&gt;&gt; set_tracking_uri(&quot;file:%s&quot;%d)</span>
<span class="sd"> &gt;&gt;&gt; client = MlflowClient()</span>
<span class="sd"> &gt;&gt;&gt; exp_id = mlflow.create_experiment(&quot;my_experiment&quot;)</span>
<span class="sd"> &gt;&gt;&gt; exp = mlflow.set_experiment(&quot;my_experiment&quot;)</span>
<span class="sd"> We aim at learning this numerical function using a simple linear regressor.</span>
<span class="sd"> &gt;&gt;&gt; from sklearn.linear_model import LinearRegression</span>
<span class="sd"> &gt;&gt;&gt; train = pd.DataFrame({&quot;x1&quot;: np.arange(8), &quot;x2&quot;: np.arange(8)**2,</span>
<span class="sd"> ... &quot;y&quot;: np.log(2 + np.arange(8))})</span>
<span class="sd"> &gt;&gt;&gt; train_x = train[[&quot;x1&quot;, &quot;x2&quot;]]</span>
<span class="sd"> &gt;&gt;&gt; train_y = train[[&quot;y&quot;]]</span>
<span class="sd"> &gt;&gt;&gt; with mlflow.start_run():</span>
<span class="sd"> ... lr = LinearRegression()</span>
<span class="sd"> ... lr.fit(train_x, train_y)</span>
<span class="sd"> ... mlflow.sklearn.log_model(lr, &quot;model&quot;)</span>
<span class="sd"> LinearRegression...</span>
<span class="sd"> Now that our model is logged using MLflow, we load it back and apply it on a pandas-on-Spark</span>
<span class="sd"> dataframe:</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.pandas.mlflow import load_model</span>
<span class="sd"> &gt;&gt;&gt; run_info = client.search_runs(exp_id)[-1].info</span>
<span class="sd"> &gt;&gt;&gt; model = load_model(&quot;runs:/{run_id}/model&quot;.format(run_id=run_info.run_id))</span>
<span class="sd"> &gt;&gt;&gt; prediction_df = ps.DataFrame({&quot;x1&quot;: [2.0], &quot;x2&quot;: [4.0]})</span>
<span class="sd"> &gt;&gt;&gt; prediction_df[&quot;prediction&quot;] = model.predict(prediction_df)</span>
<span class="sd"> &gt;&gt;&gt; prediction_df</span>
<span class="sd"> x1 x2 prediction</span>
<span class="sd"> 0 2.0 4.0 1.355551</span>
<span class="sd"> The model also works on pandas DataFrames as expected:</span>
<span class="sd"> &gt;&gt;&gt; model.predict(prediction_df[[&quot;x1&quot;, &quot;x2&quot;]].to_pandas())</span>
<span class="sd"> array([[1.35555142]])</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Currently, the model prediction can only be merged back with the existing dataframe.</span>
<span class="sd"> Other columns must be manually joined.</span>
<span class="sd"> For example, this code will not work:</span>
<span class="sd"> &gt;&gt;&gt; df = ps.DataFrame({&quot;x1&quot;: [2.0], &quot;x2&quot;: [3.0], &quot;z&quot;: [-1]})</span>
<span class="sd"> &gt;&gt;&gt; features = df[[&quot;x1&quot;, &quot;x2&quot;]]</span>
<span class="sd"> &gt;&gt;&gt; y = model.predict(features)</span>
<span class="sd"> &gt;&gt;&gt; # Works:</span>
<span class="sd"> &gt;&gt;&gt; features[&quot;y&quot;] = y # doctest: +SKIP</span>
<span class="sd"> &gt;&gt;&gt; # Will fail with a message about dataframes not aligned.</span>
<span class="sd"> &gt;&gt;&gt; df[&quot;y&quot;] = y # doctest: +SKIP</span>
<span class="sd"> A current workaround is to use the .merge() function, using the feature values</span>
<span class="sd"> as merging keys.</span>
<span class="sd"> &gt;&gt;&gt; features[&#39;y&#39;] = y</span>
<span class="sd"> &gt;&gt;&gt; everything = df.merge(features, on=[&#39;x1&#39;, &#39;x2&#39;])</span>
<span class="sd"> &gt;&gt;&gt; everything</span>
<span class="sd"> x1 x2 z y</span>
<span class="sd"> 0 2.0 3.0 -1 1.376932</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">PythonModelWrapper</span><span class="p">(</span><span class="n">model_uri</span><span class="p">,</span> <span class="n">predict_type</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span>
<span class="kn">import</span> <span class="nn">pyspark.pandas.mlflow</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SPARK_HOME&quot;</span><span class="p">])</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span><span class="o">.</span><span class="n">mlflow</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;ps&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span>
<span class="n">spark</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;pyspark.pandas.mlflow tests&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="p">)</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span>
<span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span><span class="o">.</span><span class="n">mlflow</span><span class="p">,</span>
<span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span>
<span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span> <span class="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">NORMALIZE_WHITESPACE</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">mlflow</span> <span class="c1"># noqa: F401</span>
<span class="kn">import</span> <span class="nn">sklearn</span> <span class="c1"># noqa: F401</span>
<span class="n">_test</span><span class="p">()</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="k">pass</span>
</pre></div>
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