From: hsiang41 <rick.chen@prophetstor.com>
Date: Tue, 16 Oct 2018 09:30:01 +0000 (+0800)
Subject: mgr/diskprediction: Solve predictor model file not backward compatibility
X-Git-Tag: v14.1.0~1145^2
X-Git-Url: http://git-server-git.apps.pok.os.sepia.ceph.com/?a=commitdiff_plain;h=refs%2Fpull%2F24611%2Fhead;p=ceph.git

mgr/diskprediction: Solve predictor model file not backward compatibility

Use pickle library instead of joblib library. It can avoid library not compatible with different operation system.

Signed-off-by: Rick Chen <rick.chen@prophetstor.com>
---

diff --git a/src/pybind/mgr/diskprediction/common/localpredictor.py b/src/pybind/mgr/diskprediction/common/localpredictor.py
index f2ca8f16a2d4..2a79aee6c77e 100644
--- a/src/pybind/mgr/diskprediction/common/localpredictor.py
+++ b/src/pybind/mgr/diskprediction/common/localpredictor.py
@@ -6,7 +6,7 @@ import time
 
 from . import DummyResonse
 from .clusterdata import ClusterAPI
-from ..predictor.DiskFailurePredictor import DiskFailurePredictor, get_diskfailurepredictor_path
+from ..predictor.disk_failure_predictor import DiskFailurePredictor, get_diskfailurepredictor_path
 
 
 def gen_configuration(**kwargs):
diff --git a/src/pybind/mgr/diskprediction/predictor/DiskFailurePredictor.py b/src/pybind/mgr/diskprediction/predictor/DiskFailurePredictor.py
deleted file mode 100644
index e16d3e1a203d..000000000000
--- a/src/pybind/mgr/diskprediction/predictor/DiskFailurePredictor.py
+++ /dev/null
@@ -1,257 +0,0 @@
-"""Sample code for disk failure prediction.
-
-This sample code is a community version for anyone who is interested in Machine
-Learning and care about disk failure.
-
-This class provides a disk failure prediction module. Given models dirpath to
-initialize a predictor instance and then use 6 days data to predict. Predict
-function will return a string to indicate disk failure status: "Good",
-"Warning", "Bad", or "Unknown".
-
-An example code is as follows:
-
->>> model = DiskFailurePredictor.DiskFailurePredictor()
->>> status = model.initialize("./models")
->>> if status:
->>>     model.predict(disk_days)
-'Bad'
-
-
-Provided by ProphetStor Data Services Inc.
-http://www.prophetstor.com/
-
-"""
-
-from __future__ import print_function
-import os
-import json
-from sklearn.externals import joblib
-
-
-def get_diskfailurepredictor_path():
-    path = os.path.abspath(__file__)
-    dir_path = os.path.dirname(path)
-    return dir_path
-
-
-class DiskFailurePredictor(object):
-    """Disk failure prediction
-
-    This class implements a disk failure prediction module.
-    """
-
-    CONFIG_FILE = "config.json"
-    EXCLUDED_ATTRS = ['smart_9_raw', 'smart_241_raw', 'smart_242_raw']
-
-    def __init__(self):
-        """
-        This function may throw exception due to wrong file operation.
-        """
-
-        self.model_dirpath = ""
-        self.model_context = {}
-
-    def initialize(self, model_dirpath):
-        """
-        Initialize all models.
-
-        Args: None
-
-        Returns:
-            Error message. If all goes well, return an empty string.
-
-        Raises:
-        """
-
-        config_path = os.path.join(model_dirpath, self.CONFIG_FILE)
-        if not os.path.isfile(config_path):
-            return "Missing config file: " + config_path
-        else:
-            with open(config_path) as f_conf:
-                self.model_context = json.load(f_conf)
-
-        for model_name in self.model_context:
-            model_path = os.path.join(model_dirpath, model_name)
-
-            if not os.path.isfile(model_path):
-                return "Missing model file: " + model_path
-
-        self.model_dirpath = model_dirpath
-
-    def __preprocess(self, disk_days):
-        """
-        Preprocess disk attributes.
-
-        Args:
-            disk_days: Refer to function predict(...).
-
-        Returns:
-            new_disk_days: Processed disk days.
-        """
-
-        req_attrs = []
-        new_disk_days = []
-
-        attr_list = set.intersection(*[set(disk_day.keys())
-                                       for disk_day in disk_days])
-        for attr in attr_list:
-            if (attr.startswith('smart_') and attr.endswith('_raw')) and \
-                    attr not in self.EXCLUDED_ATTRS:
-                req_attrs.append(attr)
-
-        for disk_day in disk_days:
-            new_disk_day = {}
-            for attr in req_attrs:
-                if float(disk_day[attr]) >= 0.0:
-                    new_disk_day[attr] = disk_day[attr]
-
-            new_disk_days.append(new_disk_day)
-
-        return new_disk_days
-
-    @staticmethod
-    def __get_diff_attrs(disk_days):
-        """
-        Get 5 days differential attributes.
-
-        Args:
-            disk_days: Refer to function predict(...).
-
-        Returns:
-            attr_list: All S.M.A.R.T. attributes used in given disk. Here we
-                       use intersection set of all disk days.
-
-            diff_disk_days: A list struct comprises 5 dictionaries, each
-                            dictionary contains differential attributes.
-
-        Raises:
-            Exceptions of wrong list/dict operations.
-        """
-
-        all_attrs = [set(disk_day.keys()) for disk_day in disk_days]
-        attr_list = list(set.intersection(*all_attrs))
-        attr_list = disk_days[0].keys()
-        prev_days = disk_days[:-1]
-        curr_days = disk_days[1:]
-        diff_disk_days = []
-
-        for prev, cur in zip(prev_days, curr_days):
-            diff_disk_days.append({attr:(int(cur[attr]) - int(prev[attr]))
-                                   for attr in attr_list})
-
-        return attr_list, diff_disk_days
-
-    def __get_best_models(self, attr_list):
-        """
-        Find the best model from model list according to given attribute list.
-
-        Args:
-            attr_list: All S.M.A.R.T. attributes used in given disk.
-
-        Returns:
-            modelpath: The best model for the given attribute list.
-            model_attrlist: 'Ordered' attribute list of the returned model.
-                            Must be aware that SMART attributes is in order.
-
-        Raises:
-        """
-
-        models = self.model_context.keys()
-
-        scores = []
-        for model_name in models:
-            scores.append(sum(attr in attr_list
-                              for attr in self.model_context[model_name]))
-        max_score = max(scores)
-
-        # Skip if too few matched attributes.
-        if max_score < 3:
-            print("Too few matched attributes")
-            return None
-
-        best_models = {}
-        best_model_indices = [idx for idx, score in enumerate(scores)
-                              if score > max_score - 2]
-        for model_idx in best_model_indices:
-            model_name = list(models)[model_idx]
-            model_path = os.path.join(self.model_dirpath, model_name)
-            model_attrlist = self.model_context[model_name]
-            best_models[model_path] = model_attrlist
-
-        return best_models
-        # return os.path.join(self.model_dirpath, model_name), model_attrlist
-
-    @staticmethod
-    def __get_ordered_attrs(disk_days, model_attrlist):
-        """
-        Return ordered attributes of given disk days.
-
-        Args:
-            disk_days: Unordered disk days.
-            model_attrlist: Model's ordered attribute list.
-
-        Returns:
-            ordered_attrs: Ordered disk days.
-
-        Raises: None
-        """
-
-        ordered_attrs = []
-
-        for one_day in disk_days:
-            one_day_attrs = []
-
-            for attr in model_attrlist:
-                if attr in one_day:
-                    one_day_attrs.append(one_day[attr])
-                else:
-                    one_day_attrs.append(0)
-
-            ordered_attrs.append(one_day_attrs)
-
-        return ordered_attrs
-
-    def predict(self, disk_days):
-        """
-        Predict using given 6-days disk S.M.A.R.T. attributes.
-
-        Args:
-            disk_days: A list struct comprises 6 dictionaries. These
-                       dictionaries store 'consecutive' days of disk SMART
-                       attributes.
-        Returns:
-            A string indicates prediction result. One of following four strings
-            will be returned according to disk failure status:
-            (1) Good : Disk is health
-            (2) Warning : Disk has some symptoms but may not fail immediately
-            (3) Bad : Disk is in danger and data backup is highly recommended
-            (4) Unknown : Not enough data for prediction.
-
-        Raises: None
-        """
-
-        all_pred = []
-
-        proc_disk_days = self.__preprocess(disk_days)
-        attr_list, diff_data = DiskFailurePredictor.__get_diff_attrs(proc_disk_days)
-        modellist = self.__get_best_models(attr_list)
-        if modellist is None:
-            return "Unknown"
-
-        for modelpath in modellist:
-            model_attrlist = modellist[modelpath]
-            ordered_data = DiskFailurePredictor.__get_ordered_attrs(
-                diff_data, model_attrlist)
-
-            clf = joblib.load(modelpath)
-            pred = clf.predict(ordered_data)
-
-            all_pred.append(1 if any(pred) else 0)
-
-        score = 2 ** sum(all_pred) - len(modellist)
-        if score > 10:
-            return "Bad"
-        elif score > 4:
-            return "Warning"
-        else:
-            return "Good"
diff --git a/src/pybind/mgr/diskprediction/predictor/disk_failure_predictor.py b/src/pybind/mgr/diskprediction/predictor/disk_failure_predictor.py
new file mode 100644
index 000000000000..bf9b0d7db16e
--- /dev/null
+++ b/src/pybind/mgr/diskprediction/predictor/disk_failure_predictor.py
@@ -0,0 +1,265 @@
+"""Sample code for disk failure prediction.
+
+This sample code is a community version for anyone who is interested in Machine
+Learning and care about disk failure.
+
+This class provides a disk failure prediction module. Given models dirpath to
+initialize a predictor instance and then use 6 days data to predict. Predict
+function will return a string to indicate disk failure status: "Good",
+"Warning", "Bad", or "Unknown".
+
+An example code is as follows:
+
+>>> model = disk_failure_predictor.DiskFailurePredictor()
+>>> status = model.initialize("./models")
+>>> if status:
+>>>     model.predict(disk_days)
+'Bad'
+
+
+Provided by ProphetStor Data Services Inc.
+http://www.prophetstor.com/
+
+"""
+
+from __future__ import print_function
+import os
+import json
+import pickle
+
+
+def get_diskfailurepredictor_path():
+    path = os.path.abspath(__file__)
+    dir_path = os.path.dirname(path)
+    return dir_path
+
+
+class DiskFailurePredictor(object):
+    """Disk failure prediction
+
+    This class implements a disk failure prediction module.
+    """
+
+    CONFIG_FILE = "config.json"
+    EXCLUDED_ATTRS = ['smart_9_raw', 'smart_241_raw', 'smart_242_raw']
+
+    def __init__(self):
+        """
+        This function may throw exception due to wrong file operation.
+        """
+
+        self.model_dirpath = ""
+        self.model_context = {}
+
+    def initialize(self, model_dirpath):
+        """
+        Initialize all models.
+
+        Args: None
+
+        Returns:
+            Error message. If all goes well, return an empty string.
+
+        Raises:
+        """
+
+        config_path = os.path.join(model_dirpath, self.CONFIG_FILE)
+        if not os.path.isfile(config_path):
+            return "Missing config file: " + config_path
+        else:
+            with open(config_path) as f_conf:
+                self.model_context = json.load(f_conf)
+
+        for model_name in self.model_context:
+            model_path = os.path.join(model_dirpath, model_name)
+
+            if not os.path.isfile(model_path):
+                return "Missing model file: " + model_path
+
+        self.model_dirpath = model_dirpath
+
+    def __preprocess(self, disk_days):
+        """
+        Preprocess disk attributes.
+
+        Args:
+            disk_days: Refer to function predict(...).
+
+        Returns:
+            new_disk_days: Processed disk days.
+        """
+
+        req_attrs = []
+        new_disk_days = []
+
+        attr_list = set.intersection(*[set(disk_day.keys())
+                                       for disk_day in disk_days])
+        for attr in attr_list:
+            if (attr.startswith('smart_') and attr.endswith('_raw')) and \
+                    attr not in self.EXCLUDED_ATTRS:
+                req_attrs.append(attr)
+
+        for disk_day in disk_days:
+            new_disk_day = {}
+            for attr in req_attrs:
+                if float(disk_day[attr]) >= 0.0:
+                    new_disk_day[attr] = disk_day[attr]
+
+            new_disk_days.append(new_disk_day)
+
+        return new_disk_days
+
+    @staticmethod
+    def __get_diff_attrs(disk_days):
+        """
+        Get 5 days differential attributes.
+
+        Args:
+            disk_days: Refer to function predict(...).
+
+        Returns:
+            attr_list: All S.M.A.R.T. attributes used in given disk. Here we
+                       use intersection set of all disk days.
+
+            diff_disk_days: A list struct comprises 5 dictionaries, each
+                            dictionary contains differential attributes.
+
+        Raises:
+            Exceptions of wrong list/dict operations.
+        """
+
+        all_attrs = [set(disk_day.keys()) for disk_day in disk_days]
+        attr_list = list(set.intersection(*all_attrs))
+        attr_list = disk_days[0].keys()
+        prev_days = disk_days[:-1]
+        curr_days = disk_days[1:]
+        diff_disk_days = []
+
+        for prev, cur in zip(prev_days, curr_days):
+            diff_disk_days.append({attr:(int(cur[attr]) - int(prev[attr]))
+                                   for attr in attr_list})
+
+        return attr_list, diff_disk_days
+
+    def __get_best_models(self, attr_list):
+        """
+        Find the best model from model list according to given attribute list.
+
+        Args:
+            attr_list: All S.M.A.R.T. attributes used in given disk.
+
+        Returns:
+            modelpath: The best model for the given attribute list.
+            model_attrlist: 'Ordered' attribute list of the returned model.
+                            Must be aware that SMART attributes is in order.
+
+        Raises:
+        """
+
+        models = self.model_context.keys()
+
+        scores = []
+        for model_name in models:
+            scores.append(sum(attr in attr_list
+                              for attr in self.model_context[model_name]))
+        max_score = max(scores)
+
+        # Skip if too few matched attributes.
+        if max_score < 3:
+            print("Too few matched attributes")
+            return None
+
+        best_models = {}
+        best_model_indices = [idx for idx, score in enumerate(scores)
+                              if score > max_score - 2]
+        for model_idx in best_model_indices:
+            model_name = list(models)[model_idx]
+            model_path = os.path.join(self.model_dirpath, model_name)
+            model_attrlist = self.model_context[model_name]
+            best_models[model_path] = model_attrlist
+
+        return best_models
+        # return os.path.join(self.model_dirpath, model_name), model_attrlist
+
+    @staticmethod
+    def __get_ordered_attrs(disk_days, model_attrlist):
+        """
+        Return ordered attributes of given disk days.
+
+        Args:
+            disk_days: Unordered disk days.
+            model_attrlist: Model's ordered attribute list.
+
+        Returns:
+            ordered_attrs: Ordered disk days.
+
+        Raises: None
+        """
+
+        ordered_attrs = []
+
+        for one_day in disk_days:
+            one_day_attrs = []
+
+            for attr in model_attrlist:
+                if attr in one_day:
+                    one_day_attrs.append(one_day[attr])
+                else:
+                    one_day_attrs.append(0)
+
+            ordered_attrs.append(one_day_attrs)
+
+        return ordered_attrs
+
+    def predict(self, disk_days):
+        """
+        Predict using given 6-days disk S.M.A.R.T. attributes.
+
+        Args:
+            disk_days: A list struct comprises 6 dictionaries. These
+                       dictionaries store 'consecutive' days of disk SMART
+                       attributes.
+        Returns:
+            A string indicates prediction result. One of following four strings
+            will be returned according to disk failure status:
+            (1) Good : Disk is health
+            (2) Warning : Disk has some symptoms but may not fail immediately
+            (3) Bad : Disk is in danger and data backup is highly recommended
+            (4) Unknown : Not enough data for prediction.
+
+        Raises:
+            Pickle exceptions
+        """
+
+        all_pred = []
+
+        proc_disk_days = self.__preprocess(disk_days)
+        attr_list, diff_data = DiskFailurePredictor.__get_diff_attrs(proc_disk_days)
+        modellist = self.__get_best_models(attr_list)
+        if modellist is None:
+            return "Unknown"
+
+        for modelpath in modellist:
+            model_attrlist = modellist[modelpath]
+            ordered_data = DiskFailurePredictor.__get_ordered_attrs(
+                diff_data, model_attrlist)
+
+            try:
+                with open(modelpath, 'rb') as f_model:
+                    clf = pickle.load(f_model)
+
+            except UnicodeDecodeError:
+                # Compatibility for python3
+                with open(modelpath, 'rb') as f_model:
+                    clf = pickle.load(f_model, encoding='latin1')
+
+            pred = clf.predict(ordered_data)
+
+            all_pred.append(1 if any(pred) else 0)
+
+        score = 2 ** sum(all_pred) - len(modellist)
+        if score > 10:
+            return "Bad"
+        if score > 4:
+            return "Warning"
+        return "Good"
diff --git a/src/pybind/mgr/diskprediction/predictor/models/config.json b/src/pybind/mgr/diskprediction/predictor/models/config.json
index 61439ae5e3b1..9a1485ca35dd 100644
--- a/src/pybind/mgr/diskprediction/predictor/models/config.json
+++ b/src/pybind/mgr/diskprediction/predictor/models/config.json
@@ -1,77 +1,77 @@
 {
-"svm_123.joblib": ["smart_197_raw", "smart_183_raw", "smart_200_raw", "smart_194_raw", "smart_254_raw", "smart_252_raw", "smart_4_raw", "smart_222_raw", "smart_187_raw", "smart_184_raw"],
-"svm_105.joblib": ["smart_197_raw", "smart_4_raw", "smart_5_raw", "smart_252_raw", "smart_184_raw", "smart_223_raw", "smart_198_raw", "smart_10_raw", "smart_189_raw", "smart_222_raw"],
-"svm_82.joblib":["smart_184_raw", "smart_2_raw", "smart_187_raw", "smart_225_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_13_raw", "smart_188_raw", "smart_251_raw"],
-"svm_186.joblib":["smart_3_raw", "smart_11_raw", "smart_198_raw", "smart_250_raw", "smart_13_raw", "smart_200_raw", "smart_224_raw", "smart_187_raw", "smart_22_raw", "smart_4_raw", "smart_220_raw"],
-"svm_14.joblib":["smart_12_raw", "smart_226_raw", "smart_187_raw", "smart_196_raw", "smart_5_raw", "smart_183_raw", "smart_255_raw", "smart_250_raw", "smart_201_raw", "smart_8_raw"],
-"svm_10.joblib":["smart_251_raw", "smart_4_raw", "smart_223_raw", "smart_13_raw", "smart_255_raw", "smart_188_raw", "smart_197_raw", "smart_201_raw", "smart_250_raw", "smart_15_raw"],
-"svm_235.joblib":["smart_15_raw", "smart_255_raw", "smart_252_raw", "smart_197_raw", "smart_250_raw", "smart_254_raw", "smart_13_raw", "smart_251_raw", "smart_198_raw", "smart_189_raw", "smart_191_raw"],
-"svm_234.joblib":["smart_187_raw", "smart_183_raw", "smart_3_raw", "smart_4_raw", "smart_222_raw", "smart_184_raw", "smart_5_raw", "smart_198_raw", "smart_200_raw", "smart_8_raw", "smart_10_raw"],
-"svm_119.joblib":["smart_254_raw", "smart_8_raw", "smart_183_raw", "smart_184_raw", "smart_195_raw", "smart_252_raw", "smart_191_raw", "smart_10_raw", "smart_200_raw", "smart_197_raw"],
-"svm_227.joblib":["smart_254_raw", "smart_189_raw", "smart_225_raw", "smart_224_raw", "smart_197_raw", "smart_223_raw", "smart_4_raw", "smart_183_raw", "smart_11_raw", "smart_184_raw", "smart_13_raw"],
-"svm_18.joblib":["smart_197_raw", "smart_3_raw", "smart_220_raw", "smart_193_raw", "smart_10_raw", "smart_187_raw", "smart_188_raw", "smart_225_raw", "smart_194_raw", "smart_13_raw"],
-"svm_78.joblib":["smart_10_raw", "smart_183_raw", "smart_191_raw", "smart_13_raw", "smart_198_raw", "smart_22_raw", "smart_195_raw", "smart_12_raw", "smart_224_raw", "smart_200_raw"],
-"svm_239.joblib":["smart_3_raw", "smart_254_raw", "smart_199_raw", "smart_225_raw", "smart_187_raw", "smart_195_raw", "smart_197_raw", "smart_2_raw", "smart_193_raw", "smart_220_raw", "smart_183_raw"],
-"svm_174.joblib":["smart_183_raw", "smart_196_raw", "smart_225_raw", "smart_189_raw", "smart_4_raw", "smart_3_raw", "smart_9_raw", "smart_198_raw", "smart_15_raw", "smart_5_raw", "smart_194_raw"],
-"svm_104.joblib":["smart_12_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_240_raw", "smart_187_raw", "smart_225_raw", "smart_8_raw", "smart_3_raw", "smart_2_raw"],
-"svm_12.joblib":["smart_222_raw", "smart_251_raw", "smart_194_raw", "smart_9_raw", "smart_184_raw", "smart_191_raw", "smart_187_raw", "smart_255_raw", "smart_4_raw", "smart_11_raw"],
-"svm_97.joblib":["smart_15_raw", "smart_197_raw", "smart_190_raw", "smart_199_raw", "smart_200_raw", "smart_12_raw", "smart_191_raw", "smart_254_raw", "smart_194_raw", "smart_201_raw"],
-"svm_118.joblib":["smart_11_raw", "smart_225_raw", "smart_196_raw", "smart_197_raw", "smart_198_raw", "smart_200_raw", "smart_3_raw", "smart_10_raw", "smart_191_raw", "smart_22_raw"],
-"svm_185.joblib":["smart_191_raw", "smart_254_raw", "smart_3_raw", "smart_190_raw", "smart_15_raw", "smart_22_raw", "smart_2_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_225_raw"],
-"svm_206.joblib":["smart_183_raw", "smart_192_raw", "smart_197_raw", "smart_255_raw", "smart_187_raw", "smart_254_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_240_raw", "smart_8_raw"],
-"svm_225.joblib":["smart_224_raw", "smart_11_raw", "smart_5_raw", "smart_4_raw", "smart_225_raw", "smart_197_raw", "smart_15_raw", "smart_183_raw", "smart_193_raw", "smart_190_raw", "smart_187_raw"],
-"svm_169.joblib":["smart_252_raw", "smart_183_raw", "smart_254_raw", "smart_11_raw", "smart_193_raw", "smart_22_raw", "smart_226_raw", "smart_189_raw", "smart_225_raw", "smart_198_raw", "smart_200_raw"],
-"svm_79.joblib":["smart_184_raw", "smart_196_raw", "smart_4_raw", "smart_226_raw", "smart_199_raw", "smart_187_raw", "smart_193_raw", "smart_188_raw", "smart_12_raw", "smart_250_raw"],
-"svm_69.joblib":["smart_187_raw", "smart_9_raw", "smart_200_raw", "smart_11_raw", "smart_252_raw", "smart_189_raw", "smart_4_raw", "smart_188_raw", "smart_255_raw", "smart_201_raw"],
-"svm_201.joblib":["smart_224_raw", "smart_8_raw", "smart_250_raw", "smart_2_raw", "smart_198_raw", "smart_15_raw", "smart_193_raw", "smart_223_raw", "smart_3_raw", "smart_11_raw", "smart_191_raw"],
-"svm_114.joblib":["smart_226_raw", "smart_188_raw", "smart_2_raw", "smart_11_raw", "smart_4_raw", "smart_193_raw", "smart_184_raw", "smart_194_raw", "smart_198_raw", "smart_13_raw"],
-"svm_219.joblib":["smart_12_raw", "smart_22_raw", "smart_8_raw", "smart_191_raw", "smart_197_raw", "smart_254_raw", "smart_15_raw", "smart_193_raw", "smart_199_raw", "smart_225_raw", "smart_192_raw"],
-"svm_168.joblib":["smart_255_raw", "smart_191_raw", "smart_193_raw", "smart_220_raw", "smart_5_raw", "smart_3_raw", "smart_222_raw", "smart_223_raw", "smart_197_raw", "smart_196_raw", "smart_22_raw"],
-"svm_243.joblib":["smart_11_raw", "smart_255_raw", "smart_10_raw", "smart_189_raw", "smart_225_raw", "smart_240_raw", "smart_222_raw", "smart_197_raw", "smart_183_raw", "smart_198_raw", "smart_12_raw"],
-"svm_195.joblib":["smart_183_raw", "smart_5_raw", "smart_11_raw", "smart_197_raw", "smart_15_raw", "smart_9_raw", "smart_4_raw", "smart_220_raw", "smart_12_raw", "smart_192_raw", "smart_240_raw"],
-"svm_222.joblib":["smart_10_raw", "smart_13_raw", "smart_188_raw", "smart_15_raw", "smart_192_raw", "smart_224_raw", "smart_225_raw", "smart_187_raw", "smart_222_raw", "smart_220_raw", "smart_252_raw"],
-"svm_62.joblib":["smart_196_raw", "smart_251_raw", "smart_187_raw", "smart_224_raw", "smart_11_raw", "smart_12_raw", "smart_8_raw", "smart_199_raw", "smart_220_raw", "smart_195_raw"],
-"svm_151.joblib":["smart_187_raw", "smart_223_raw", "smart_200_raw", "smart_189_raw", "smart_251_raw", "smart_255_raw", "smart_222_raw", "smart_192_raw", "smart_12_raw", "smart_183_raw", "smart_22_raw"],
-"svm_125.joblib":["smart_9_raw", "smart_252_raw", "smart_197_raw", "smart_251_raw", "smart_11_raw", "smart_12_raw", "smart_188_raw", "smart_240_raw", "smart_10_raw", "smart_223_raw"],
-"svm_124.joblib":["smart_193_raw", "smart_187_raw", "smart_183_raw", "smart_11_raw", "smart_10_raw", "smart_8_raw", "smart_194_raw", "smart_189_raw", "smart_222_raw", "smart_191_raw"],
-"svm_67.joblib":["smart_2_raw", "smart_8_raw", "smart_225_raw", "smart_240_raw", "smart_13_raw", "smart_5_raw", "smart_187_raw", "smart_198_raw", "smart_199_raw", "smart_3_raw"],
-"svm_115.joblib":["smart_222_raw", "smart_193_raw", "smart_223_raw", "smart_195_raw", "smart_252_raw", "smart_189_raw", "smart_199_raw", "smart_187_raw", "smart_15_raw", "smart_184_raw"],
-"svm_1.joblib":["smart_201_raw", "smart_8_raw", "smart_200_raw", "smart_252_raw", "smart_251_raw", "smart_187_raw", "smart_9_raw", "smart_188_raw", "smart_15_raw", "smart_184_raw"],
-"svm_112.joblib":["smart_220_raw", "smart_197_raw", "smart_10_raw", "smart_188_raw", "smart_12_raw", "smart_4_raw", "smart_196_raw", "smart_3_raw", "smart_240_raw", "smart_225_raw"],
-"svm_138.joblib":["smart_183_raw", "smart_10_raw", "smart_191_raw", "smart_195_raw", "smart_223_raw", "smart_189_raw", "smart_187_raw", "smart_255_raw", "smart_226_raw", "smart_8_raw"],
-"svm_229.joblib":["smart_224_raw", "smart_8_raw", "smart_192_raw", "smart_220_raw", "smart_195_raw", "smart_183_raw", "smart_250_raw", "smart_187_raw", "smart_225_raw", "smart_4_raw", "smart_252_raw"],
-"svm_145.joblib":["smart_190_raw", "smart_8_raw", "smart_226_raw", "smart_184_raw", "smart_225_raw", "smart_220_raw", "smart_193_raw", "smart_183_raw", "smart_201_raw", "smart_187_raw", "smart_2_raw"],
-"svm_59.joblib":["smart_188_raw", "smart_11_raw", "smart_184_raw", "smart_2_raw", "smart_220_raw", "smart_198_raw", "smart_225_raw", "smart_240_raw", "smart_197_raw", "smart_251_raw"],
-"svm_204.joblib":["smart_15_raw", "smart_240_raw", "smart_225_raw", "smart_223_raw", "smart_252_raw", "smart_22_raw", "smart_200_raw", "smart_13_raw", "smart_220_raw", "smart_198_raw", "smart_191_raw"],
-"svm_88.joblib":["smart_198_raw", "smart_3_raw", "smart_8_raw", "smart_225_raw", "smart_251_raw", "smart_222_raw", "smart_188_raw", "smart_10_raw", "smart_240_raw", "smart_189_raw"],
-"svm_182.joblib":["smart_10_raw", "smart_190_raw", "smart_250_raw", "smart_15_raw", "smart_193_raw", "smart_22_raw", "smart_200_raw", "smart_8_raw", "smart_4_raw", "smart_187_raw", "smart_9_raw"],
-"svm_61.joblib":["smart_5_raw", "smart_12_raw", "smart_9_raw", "smart_198_raw", "smart_195_raw", "smart_252_raw", "smart_15_raw", "smart_240_raw", "smart_255_raw", "smart_224_raw"],
-"svm_50.joblib":["smart_220_raw", "smart_5_raw", "smart_194_raw", "smart_250_raw", "smart_15_raw", "smart_240_raw", "smart_8_raw", "smart_198_raw", "smart_224_raw", "smart_191_raw"],
-"svm_210.joblib":["smart_8_raw", "smart_15_raw", "smart_195_raw", "smart_224_raw", "smart_5_raw", "smart_191_raw", "smart_198_raw", "smart_225_raw", "smart_200_raw", "smart_251_raw", "smart_240_raw"],
-"svm_16.joblib":["smart_222_raw", "smart_10_raw", "smart_250_raw", "smart_189_raw", "smart_191_raw", "smart_2_raw", "smart_5_raw", "smart_193_raw", "smart_9_raw", "smart_187_raw"],
-"svm_85.joblib":["smart_252_raw", "smart_184_raw", "smart_9_raw", "smart_5_raw", "smart_254_raw", "smart_3_raw", "smart_195_raw", "smart_10_raw", "smart_12_raw", "smart_222_raw"],
-"svm_36.joblib":["smart_201_raw", "smart_251_raw", "smart_184_raw", "smart_3_raw", "smart_5_raw", "smart_183_raw", "smart_194_raw", "smart_195_raw", "smart_224_raw", "smart_2_raw"],
-"svm_33.joblib":["smart_223_raw", "smart_254_raw", "smart_225_raw", "smart_9_raw", "smart_199_raw", "smart_5_raw", "smart_189_raw", "smart_194_raw", "smart_240_raw", "smart_4_raw"],
-"svm_3.joblib":["smart_225_raw", "smart_194_raw", "smart_3_raw", "smart_189_raw", "smart_9_raw", "smart_254_raw", "smart_240_raw", "smart_5_raw", "smart_255_raw", "smart_223_raw"],
-"svm_93.joblib":["smart_8_raw", "smart_188_raw", "smart_5_raw", "smart_10_raw", "smart_222_raw", "smart_2_raw", "smart_254_raw", "smart_12_raw", "smart_193_raw", "smart_224_raw"],
-"svm_120.joblib":["smart_189_raw", "smart_224_raw", "smart_222_raw", "smart_193_raw", "smart_5_raw", "smart_201_raw", "smart_8_raw", "smart_254_raw", "smart_194_raw", "smart_22_raw"],
-"svm_128.joblib":["smart_195_raw", "smart_184_raw", "smart_251_raw", "smart_8_raw", "smart_5_raw", "smart_196_raw", "smart_10_raw", "smart_4_raw", "smart_225_raw", "smart_191_raw"],
-"svm_212.joblib":["smart_225_raw", "smart_192_raw", "smart_10_raw", "smart_12_raw", "smart_222_raw", "smart_184_raw", "smart_13_raw", "smart_226_raw", "smart_5_raw", "smart_201_raw", "smart_22_raw"],
-"svm_221.joblib":["smart_255_raw", "smart_2_raw", "smart_224_raw", "smart_192_raw", "smart_252_raw", "smart_13_raw", "smart_183_raw", "smart_193_raw", "smart_15_raw", "smart_199_raw", "smart_200_raw"],
-"svm_223.joblib":["smart_4_raw", "smart_194_raw", "smart_9_raw", "smart_255_raw", "smart_188_raw", "smart_201_raw", "smart_3_raw", "smart_226_raw", "smart_192_raw", "smart_251_raw", "smart_191_raw"],
-"svm_44.joblib":["smart_255_raw", "smart_11_raw", "smart_200_raw", "smart_3_raw", "smart_195_raw", "smart_201_raw", "smart_4_raw", "smart_5_raw", "smart_10_raw", "smart_191_raw"],
-"svm_213.joblib":["smart_22_raw", "smart_191_raw", "smart_183_raw", "smart_4_raw", "smart_194_raw", "smart_255_raw", "smart_254_raw", "smart_193_raw", "smart_11_raw", "smart_10_raw", "smart_220_raw"],
-"svm_131.joblib":["smart_22_raw", "smart_194_raw", "smart_184_raw", "smart_250_raw", "smart_10_raw", "smart_189_raw", "smart_183_raw", "smart_240_raw", "smart_12_raw", "smart_252_raw"],
-"svm_6.joblib":["smart_194_raw", "smart_250_raw", "smart_223_raw", "smart_224_raw", "smart_184_raw", "smart_191_raw", "smart_201_raw", "smart_9_raw", "smart_252_raw", "smart_3_raw"],
-"svm_161.joblib":["smart_255_raw", "smart_222_raw", "smart_226_raw", "smart_254_raw", "smart_183_raw", "smart_22_raw", "smart_12_raw", "smart_190_raw", "smart_11_raw", "smart_192_raw", "smart_251_raw"],
-"svm_72.joblib":["smart_13_raw", "smart_184_raw", "smart_223_raw", "smart_240_raw", "smart_250_raw", "smart_251_raw", "smart_201_raw", "smart_196_raw", "smart_5_raw", "smart_4_raw"],
-"svm_27.joblib":["smart_189_raw", "smart_188_raw", "smart_255_raw", "smart_251_raw", "smart_240_raw", "smart_15_raw", "smart_9_raw", "smart_191_raw", "smart_226_raw", "smart_10_raw"],
-"svm_141.joblib":["smart_9_raw", "smart_191_raw", "smart_2_raw", "smart_226_raw", "smart_13_raw", "smart_22_raw", "smart_193_raw", "smart_222_raw", "smart_220_raw", "smart_225_raw", "smart_3_raw"],
-"svm_57.joblib":["smart_12_raw", "smart_252_raw", "smart_190_raw", "smart_226_raw", "smart_10_raw", "smart_189_raw", "smart_193_raw", "smart_2_raw", "smart_9_raw", "smart_223_raw"],
-"svm_236.joblib":["smart_200_raw", "smart_189_raw", "smart_226_raw", "smart_252_raw", "smart_250_raw", "smart_193_raw", "smart_13_raw", "smart_2_raw", "smart_254_raw", "smart_22_raw", "smart_9_raww"],
-"svm_208.joblib":["smart_223_raw", "smart_15_raw", "smart_251_raw", "smart_5_raw", "smart_198_raw", "smart_252_raw", "smart_4_raw", "smart_8_raw", "smart_220_raw", "smart_254_raw", "smart_193_raw"],
-"svm_230.joblib":["smart_184_raw", "smart_5_raw", "smart_191_raw", "smart_198_raw", "smart_11_raw", "smart_255_raw", "smart_189_raw", "smart_254_raw", "smart_196_raw", "smart_199_raw", "smart_223_raw"],
-"svm_134.joblib":["smart_8_raw", "smart_194_raw", "smart_4_raw", "smart_189_raw", "smart_223_raw", "smart_5_raw", "smart_187_raw", "smart_9_raw", "smart_192_raw", "smart_220_raw"],
-"svm_71.joblib":["smart_220_raw", "smart_13_raw", "smart_194_raw", "smart_197_raw", "smart_192_raw", "smart_22_raw", "smart_184_raw", "smart_199_raw", "smart_222_raw", "smart_183_raw"],
-"svm_109.joblib":["smart_224_raw", "smart_252_raw", "smart_2_raw", "smart_200_raw", "smart_5_raw", "smart_194_raw", "smart_222_raw", "smart_198_raw", "smart_4_raw", "smart_13_raw"]
+"svm_123.pkl": ["smart_197_raw", "smart_183_raw", "smart_200_raw", "smart_194_raw", "smart_254_raw", "smart_252_raw", "smart_4_raw", "smart_222_raw", "smart_187_raw", "smart_184_raw"],
+"svm_105.pkl": ["smart_197_raw", "smart_4_raw", "smart_5_raw", "smart_252_raw", "smart_184_raw", "smart_223_raw", "smart_198_raw", "smart_10_raw", "smart_189_raw", "smart_222_raw"],
+"svm_82.pkl":["smart_184_raw", "smart_2_raw", "smart_187_raw", "smart_225_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_13_raw", "smart_188_raw", "smart_251_raw"],
+"svm_186.pkl":["smart_3_raw", "smart_11_raw", "smart_198_raw", "smart_250_raw", "smart_13_raw", "smart_200_raw", "smart_224_raw", "smart_187_raw", "smart_22_raw", "smart_4_raw", "smart_220_raw"],
+"svm_14.pkl":["smart_12_raw", "smart_226_raw", "smart_187_raw", "smart_196_raw", "smart_5_raw", "smart_183_raw", "smart_255_raw", "smart_250_raw", "smart_201_raw", "smart_8_raw"],
+"svm_10.pkl":["smart_251_raw", "smart_4_raw", "smart_223_raw", "smart_13_raw", "smart_255_raw", "smart_188_raw", "smart_197_raw", "smart_201_raw", "smart_250_raw", "smart_15_raw"],
+"svm_235.pkl":["smart_15_raw", "smart_255_raw", "smart_252_raw", "smart_197_raw", "smart_250_raw", "smart_254_raw", "smart_13_raw", "smart_251_raw", "smart_198_raw", "smart_189_raw", "smart_191_raw"],
+"svm_234.pkl":["smart_187_raw", "smart_183_raw", "smart_3_raw", "smart_4_raw", "smart_222_raw", "smart_184_raw", "smart_5_raw", "smart_198_raw", "smart_200_raw", "smart_8_raw", "smart_10_raw"],
+"svm_119.pkl":["smart_254_raw", "smart_8_raw", "smart_183_raw", "smart_184_raw", "smart_195_raw", "smart_252_raw", "smart_191_raw", "smart_10_raw", "smart_200_raw", "smart_197_raw"],
+"svm_227.pkl":["smart_254_raw", "smart_189_raw", "smart_225_raw", "smart_224_raw", "smart_197_raw", "smart_223_raw", "smart_4_raw", "smart_183_raw", "smart_11_raw", "smart_184_raw", "smart_13_raw"],
+"svm_18.pkl":["smart_197_raw", "smart_3_raw", "smart_220_raw", "smart_193_raw", "smart_10_raw", "smart_187_raw", "smart_188_raw", "smart_225_raw", "smart_194_raw", "smart_13_raw"],
+"svm_78.pkl":["smart_10_raw", "smart_183_raw", "smart_191_raw", "smart_13_raw", "smart_198_raw", "smart_22_raw", "smart_195_raw", "smart_12_raw", "smart_224_raw", "smart_200_raw"],
+"svm_239.pkl":["smart_3_raw", "smart_254_raw", "smart_199_raw", "smart_225_raw", "smart_187_raw", "smart_195_raw", "smart_197_raw", "smart_2_raw", "smart_193_raw", "smart_220_raw", "smart_183_raw"],
+"svm_174.pkl":["smart_183_raw", "smart_196_raw", "smart_225_raw", "smart_189_raw", "smart_4_raw", "smart_3_raw", "smart_9_raw", "smart_198_raw", "smart_15_raw", "smart_5_raw", "smart_194_raw"],
+"svm_104.pkl":["smart_12_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_240_raw", "smart_187_raw", "smart_225_raw", "smart_8_raw", "smart_3_raw", "smart_2_raw"],
+"svm_12.pkl":["smart_222_raw", "smart_251_raw", "smart_194_raw", "smart_9_raw", "smart_184_raw", "smart_191_raw", "smart_187_raw", "smart_255_raw", "smart_4_raw", "smart_11_raw"],
+"svm_97.pkl":["smart_15_raw", "smart_197_raw", "smart_190_raw", "smart_199_raw", "smart_200_raw", "smart_12_raw", "smart_191_raw", "smart_254_raw", "smart_194_raw", "smart_201_raw"],
+"svm_118.pkl":["smart_11_raw", "smart_225_raw", "smart_196_raw", "smart_197_raw", "smart_198_raw", "smart_200_raw", "smart_3_raw", "smart_10_raw", "smart_191_raw", "smart_22_raw"],
+"svm_185.pkl":["smart_191_raw", "smart_254_raw", "smart_3_raw", "smart_190_raw", "smart_15_raw", "smart_22_raw", "smart_2_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_225_raw"],
+"svm_206.pkl":["smart_183_raw", "smart_192_raw", "smart_197_raw", "smart_255_raw", "smart_187_raw", "smart_254_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_240_raw", "smart_8_raw"],
+"svm_225.pkl":["smart_224_raw", "smart_11_raw", "smart_5_raw", "smart_4_raw", "smart_225_raw", "smart_197_raw", "smart_15_raw", "smart_183_raw", "smart_193_raw", "smart_190_raw", "smart_187_raw"],
+"svm_169.pkl":["smart_252_raw", "smart_183_raw", "smart_254_raw", "smart_11_raw", "smart_193_raw", "smart_22_raw", "smart_226_raw", "smart_189_raw", "smart_225_raw", "smart_198_raw", "smart_200_raw"],
+"svm_79.pkl":["smart_184_raw", "smart_196_raw", "smart_4_raw", "smart_226_raw", "smart_199_raw", "smart_187_raw", "smart_193_raw", "smart_188_raw", "smart_12_raw", "smart_250_raw"],
+"svm_69.pkl":["smart_187_raw", "smart_9_raw", "smart_200_raw", "smart_11_raw", "smart_252_raw", "smart_189_raw", "smart_4_raw", "smart_188_raw", "smart_255_raw", "smart_201_raw"],
+"svm_201.pkl":["smart_224_raw", "smart_8_raw", "smart_250_raw", "smart_2_raw", "smart_198_raw", "smart_15_raw", "smart_193_raw", "smart_223_raw", "smart_3_raw", "smart_11_raw", "smart_191_raw"],
+"svm_114.pkl":["smart_226_raw", "smart_188_raw", "smart_2_raw", "smart_11_raw", "smart_4_raw", "smart_193_raw", "smart_184_raw", "smart_194_raw", "smart_198_raw", "smart_13_raw"],
+"svm_219.pkl":["smart_12_raw", "smart_22_raw", "smart_8_raw", "smart_191_raw", "smart_197_raw", "smart_254_raw", "smart_15_raw", "smart_193_raw", "smart_199_raw", "smart_225_raw", "smart_192_raw"],
+"svm_168.pkl":["smart_255_raw", "smart_191_raw", "smart_193_raw", "smart_220_raw", "smart_5_raw", "smart_3_raw", "smart_222_raw", "smart_223_raw", "smart_197_raw", "smart_196_raw", "smart_22_raw"],
+"svm_243.pkl":["smart_11_raw", "smart_255_raw", "smart_10_raw", "smart_189_raw", "smart_225_raw", "smart_240_raw", "smart_222_raw", "smart_197_raw", "smart_183_raw", "smart_198_raw", "smart_12_raw"],
+"svm_195.pkl":["smart_183_raw", "smart_5_raw", "smart_11_raw", "smart_197_raw", "smart_15_raw", "smart_9_raw", "smart_4_raw", "smart_220_raw", "smart_12_raw", "smart_192_raw", "smart_240_raw"],
+"svm_222.pkl":["smart_10_raw", "smart_13_raw", "smart_188_raw", "smart_15_raw", "smart_192_raw", "smart_224_raw", "smart_225_raw", "smart_187_raw", "smart_222_raw", "smart_220_raw", "smart_252_raw"],
+"svm_62.pkl":["smart_196_raw", "smart_251_raw", "smart_187_raw", "smart_224_raw", "smart_11_raw", "smart_12_raw", "smart_8_raw", "smart_199_raw", "smart_220_raw", "smart_195_raw"],
+"svm_151.pkl":["smart_187_raw", "smart_223_raw", "smart_200_raw", "smart_189_raw", "smart_251_raw", "smart_255_raw", "smart_222_raw", "smart_192_raw", "smart_12_raw", "smart_183_raw", "smart_22_raw"],
+"svm_125.pkl":["smart_9_raw", "smart_252_raw", "smart_197_raw", "smart_251_raw", "smart_11_raw", "smart_12_raw", "smart_188_raw", "smart_240_raw", "smart_10_raw", "smart_223_raw"],
+"svm_124.pkl":["smart_193_raw", "smart_187_raw", "smart_183_raw", "smart_11_raw", "smart_10_raw", "smart_8_raw", "smart_194_raw", "smart_189_raw", "smart_222_raw", "smart_191_raw"],
+"svm_67.pkl":["smart_2_raw", "smart_8_raw", "smart_225_raw", "smart_240_raw", "smart_13_raw", "smart_5_raw", "smart_187_raw", "smart_198_raw", "smart_199_raw", "smart_3_raw"],
+"svm_115.pkl":["smart_222_raw", "smart_193_raw", "smart_223_raw", "smart_195_raw", "smart_252_raw", "smart_189_raw", "smart_199_raw", "smart_187_raw", "smart_15_raw", "smart_184_raw"],
+"svm_1.pkl":["smart_201_raw", "smart_8_raw", "smart_200_raw", "smart_252_raw", "smart_251_raw", "smart_187_raw", "smart_9_raw", "smart_188_raw", "smart_15_raw", "smart_184_raw"],
+"svm_112.pkl":["smart_220_raw", "smart_197_raw", "smart_10_raw", "smart_188_raw", "smart_12_raw", "smart_4_raw", "smart_196_raw", "smart_3_raw", "smart_240_raw", "smart_225_raw"],
+"svm_138.pkl":["smart_183_raw", "smart_10_raw", "smart_191_raw", "smart_195_raw", "smart_223_raw", "smart_189_raw", "smart_187_raw", "smart_255_raw", "smart_226_raw", "smart_8_raw"],
+"svm_229.pkl":["smart_224_raw", "smart_8_raw", "smart_192_raw", "smart_220_raw", "smart_195_raw", "smart_183_raw", "smart_250_raw", "smart_187_raw", "smart_225_raw", "smart_4_raw", "smart_252_raw"],
+"svm_145.pkl":["smart_190_raw", "smart_8_raw", "smart_226_raw", "smart_184_raw", "smart_225_raw", "smart_220_raw", "smart_193_raw", "smart_183_raw", "smart_201_raw", "smart_187_raw", "smart_2_raw"],
+"svm_59.pkl":["smart_188_raw", "smart_11_raw", "smart_184_raw", "smart_2_raw", "smart_220_raw", "smart_198_raw", "smart_225_raw", "smart_240_raw", "smart_197_raw", "smart_251_raw"],
+"svm_204.pkl":["smart_15_raw", "smart_240_raw", "smart_225_raw", "smart_223_raw", "smart_252_raw", "smart_22_raw", "smart_200_raw", "smart_13_raw", "smart_220_raw", "smart_198_raw", "smart_191_raw"],
+"svm_88.pkl":["smart_198_raw", "smart_3_raw", "smart_8_raw", "smart_225_raw", "smart_251_raw", "smart_222_raw", "smart_188_raw", "smart_10_raw", "smart_240_raw", "smart_189_raw"],
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+"svm_71.pkl":["smart_220_raw", "smart_13_raw", "smart_194_raw", "smart_197_raw", "smart_192_raw", "smart_22_raw", "smart_184_raw", "smart_199_raw", "smart_222_raw", "smart_183_raw"],
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 }
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