diff --git a/data/benchmark/auto_price.csv b/data/benchmark/auto_price.csv
new file mode 100644
index 0000000..4e073ad
--- /dev/null
+++ b/data/benchmark/auto_price.csv
@@ -0,0 +1,160 @@
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diff --git a/data/benchmark/new_thyroid.csv b/data/benchmark/new_thyroid.csv
new file mode 100644
index 0000000..2c6a086
--- /dev/null
+++ b/data/benchmark/new_thyroid.csv
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diff --git a/tests/test_Integrational.py b/tests/test_Integrational.py
new file mode 100644
index 0000000..013da0c
--- /dev/null
+++ b/tests/test_Integrational.py
@@ -0,0 +1,277 @@
+import pytest
+import itertools
+import bamt.networks as networks
+import bamt.preprocessors as pp
+from pgmpy.estimators import K2Score
+import pandas as pd
+import numpy as np
+from pandas.testing import assert_frame_equal
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+
+
+class Builder:
+    def __init__(self):
+        self.data_paths = {
+            "Continuous": "data/benchmark/auto_price.csv",
+            "Discrete": "tests/hack_discrete/hack_data.csv",
+            "Hybrid": "data/benchmark/new_thyroid.csv",
+        }
+
+        self.tail = {
+            "Continuous": ["Continuous", "target"],
+            "Discrete": ["Discrete", "Tectonic regime"],
+            "Hybrid": ["Hybrid", "target"],
+        }
+
+        self.scoring = [("K2", K2Score), "BIC", "MI"]
+        self.optimizer = ["HC"]
+        self.use_mixture = [False, True]
+        self.has_logit = [False, True]
+
+        self.static = {}
+
+        self.dynamic = {
+            "Continuous": [self.use_mixture, [False], self.optimizer, self.scoring],
+            "Discrete": [[False], [False], self.optimizer, self.scoring],
+            "Hybrid": [self.use_mixture, self.has_logit, self.optimizer, self.scoring],
+        }
+
+    def create_from_config(self):
+        """Method to collect data from config"""
+        self.static = dict(
+            Discrete=[self.data_paths["Discrete"], *self.tail["Discrete"]],
+            Continuous=[self.data_paths["Continuous"], *self.tail["Continuous"]],
+            Hybrid=[self.data_paths["Hybrid"], *self.tail["Hybrid"]],
+            evo=[False, False, "Evo", self.scoring[0]],
+        )
+
+    def create_evo_item(self, net_type):
+        evo_item = self.static["evo"][:]
+        evo_item.insert(0, self.data_paths[net_type])
+        evo_item.extend(self.tail[net_type])
+        return evo_item
+
+    @staticmethod
+    def insert_list(loc, what, to):
+        new = to[:]
+        new[loc:loc] = what
+        return new
+
+    def create_net_items(self, net_type):
+        static = self.static[net_type][:]
+        dynamic_part = map(list, itertools.product(*self.dynamic[net_type]))
+        return list(map(lambda x: self.insert_list(1, x, static), dynamic_part))
+
+    def get_params(self):
+        self.create_from_config()
+        params = []
+        for net_type in ["Discrete", "Continuous", "Hybrid"]:
+            params.extend(
+                self.create_net_items(net_type) + [self.create_evo_item(net_type)]
+            )
+        return params
+
+
+params = Builder().get_params()
+
+
+def initialize_bn(bn_type, use_mixture, has_logit):
+    if bn_type == "Discrete":
+        bn = networks.DiscreteBN()
+    elif bn_type == "Continuous":
+        bn = networks.ContinuousBN(use_mixture=use_mixture)
+    elif bn_type == "Hybrid":
+        bn = networks.HybridBN(has_logit=has_logit, use_mixture=use_mixture)
+    elif bn_type == "Composite":
+        bn = networks.CompositeBN()
+    return bn
+
+
+def prepare_data(directory):
+    data = pd.read_csv(directory, index_col=0)
+    train, test = train_test_split(data, test_size=0.33, random_state=42)
+
+    encoder = preprocessing.LabelEncoder()
+    discretizer = preprocessing.KBinsDiscretizer(
+        n_bins=5, encode="ordinal", strategy="quantile"
+    )
+
+    p = pp.Preprocessor([("encoder", encoder), ("discretizer", discretizer)])
+    discretized_data, est = p.apply(train)
+    info = p.info
+    return info, discretized_data, train, test
+
+
+class TestNetwork:
+    # Checking the equality of predictions (trained and loaded network) before and after saving
+    @pytest.mark.parametrize(
+        "directory, use_mixture, has_logit, optimizer, scoring, bn_type, target", params
+    )
+    def test_1(
+        self, directory, use_mixture, has_logit, optimizer, scoring, bn_type, target
+    ):
+        test_id = "test_1"
+
+        bn = initialize_bn(bn_type, use_mixture, has_logit)
+        info, discretized_data, train, test = prepare_data(directory)
+        bn.add_nodes(info)
+        if bn_type != "Composite":
+            bn.add_edges(
+                discretized_data,
+                optimizer=optimizer,
+                scoring_function=scoring,
+                progress_bar=False,
+            )
+        else:
+            bn.add_edges(train)
+
+        bn.fit_parameters(train)
+        predict = bn.predict(
+            test[[x for x in test.columns if x != target]], progress_bar=False
+        )
+        bn.save("bn")
+
+        bn = initialize_bn(bn_type, use_mixture, has_logit)
+        bn.load("bn.json")
+        predict_loaded = bn.predict(
+            test[[x for x in test.columns if x != target]], progress_bar=False
+        )
+
+        try:
+            assert_frame_equal(pd.DataFrame(predict), pd.DataFrame(predict_loaded))
+            print(f"{test_id} runned successfully")
+        except AssertionError:
+            print(
+                f"params: {dict(zip(['use_mixture', 'has_logit', 'optimizer', 'scoring', 'bn_type'], use_mixture, has_logit, optimizer, scoring, bn_type))}"
+            )
+            raise
+
+    # Checking the prediction algorithm (trained network) before and after saving
+    @pytest.mark.parametrize(
+        "directory, use_mixture, has_logit, optimizer, scoring, bn_type, target", params
+    )
+    def test_2(
+        self, directory, use_mixture, has_logit, optimizer, scoring, bn_type, target
+    ):
+        test_id = "test_2"
+
+        bn = initialize_bn(bn_type, use_mixture, has_logit)
+        info, discretized_data, train, test = prepare_data(directory)
+        bn.add_nodes(info)
+        if bn_type != "Composite":
+            bn.add_edges(
+                discretized_data,
+                optimizer=optimizer,
+                scoring_function=scoring,
+                progress_bar=False,
+            )
+        else:
+            bn.add_edges(train)
+
+        bn.fit_parameters(train)
+        predict = bn.predict(
+            test[[x for x in test.columns if x != target]], progress_bar=False
+        )
+        bn.save("bn")
+
+        predict2 = bn.predict(
+            test[[x for x in test.columns if x != target]], progress_bar=False
+        )
+
+        try:
+            assert_frame_equal(pd.DataFrame(predict), pd.DataFrame(predict2))
+            print(f"{test_id} runned successfully")
+        except AssertionError:
+            print(
+                f"params: {dict(zip(['use_mixture', 'has_logit', 'optimizer', 'scoring', 'bn_type'], use_mixture, has_logit, optimizer, scoring, bn_type))}"
+            )
+            raise
+
+    # Checking network predictions without edges
+    @pytest.mark.parametrize(
+        "directory, use_mixture, has_logit, optimizer, scoring, bn_type, target", params
+    )
+    def test_3(
+        self, directory, use_mixture, has_logit, optimizer, scoring, bn_type, target
+    ):
+        test_id = "test_3"
+
+        bn = initialize_bn(bn_type, use_mixture, has_logit)
+        info, discretized_data, train, test = prepare_data(directory)
+        bn.add_nodes(info)
+        bn.fit_parameters(train)
+
+        predict = bn.predict(
+            test[[x for x in test.columns if x != target]], progress_bar=False
+        )
+
+        try:
+            if info["types"][target] == "cont":
+                if use_mixture:
+                    mean = bn.distributions[target]["mean"]
+                    w = bn.distributions[target]["coef"]
+                    sample = 0
+                    for ind, wi in enumerate(w):
+                        sample += wi * mean[ind][0]
+                else:
+                    sample = train[target].mean()
+
+                assert np.all(np.array(predict[target]) == sample)
+
+            elif info["types"][target] == "disc_num":
+                most_frequent = train[target].value_counts().index[0]
+                assert np.all(np.array(predict[target]) == most_frequent)
+
+            print(f"{test_id} runned successfully")
+        except AssertionError:
+            print(
+                f"params: {dict(zip(['use_mixture', 'has_logit', 'optimizer', 'scoring', 'bn_type'], use_mixture, has_logit, optimizer, scoring, bn_type))}"
+            )
+            raise
+
+    # Checking the network trained on the 1 sample
+    @pytest.mark.parametrize(
+        "directory, use_mixture, has_logit, optimizer, scoring, bn_type, target", params
+    )
+    def test_4(
+        self, directory, use_mixture, has_logit, optimizer, scoring, bn_type, target
+    ):
+        test_id = "test_4"
+
+        if use_mixture == False:
+
+            bn = initialize_bn(bn_type, use_mixture, has_logit)
+            info, discretized_data, train, test = prepare_data(directory)
+
+            bn.add_nodes(info)
+
+            train_data_1 = pd.DataFrame(train.iloc[0].to_dict(), index=[0])
+            disc_data_1 = pd.DataFrame(discretized_data.iloc[0].to_dict(), index=[0])
+
+            if bn_type != "Composite":
+                bn.add_edges(
+                    disc_data_1,
+                    optimizer=optimizer,
+                    scoring_function=scoring,
+                    progress_bar=False,
+                )
+            else:
+                bn.add_edges(train_data_1)
+
+            bn.fit_parameters(train_data_1)
+
+            predict = bn.predict(
+                test[[x for x in test.columns if x != target]], progress_bar=False
+            )
+
+            try:
+                assert np.all(np.array(predict[target]) == train_data_1[target][0])
+                print(f"{test_id} runned successfully")
+            except AssertionError:
+                print(
+                    f"params: {dict(zip(['use_mixture', 'has_logit', 'optimizer', 'scoring', 'bn_type'], use_mixture, has_logit, optimizer, scoring, bn_type))}"
+                )
+                raise
+        else:
+            pass