Make readme examples more brief

Author: Oxid15

README.md

@@ -34,35 +34,26 @@ To track changes and version everything about data Cascade has Datasets - specia
 that encapsulate changes that are done during preprocessing.
 
 ```python
+from pprint import pprint
 from cascade import data as cdd
-
 from sklearn.datasets import load_digits
 import numpy as np
 
 
-# Load dataset
 X, y = load_digits(return_X_y=True)
 pairs = [(x, y) for (x, y) in zip(X, y)]
 
-# To track all preparation stages we wrap cdd.Dataset over
-# collection of items and targets
+# To track all preparation stages we wrap cdd.Dataset
 ds = cdd.Wrapper(pairs)
 
-# Let's make a pipeline - shuffle the dataset
+# This creates pipeline
 ds = cdd.RandomSampler(ds)
-
-# Splitting the data is also tracked in pipeline's metadata
 train_ds, test_ds = cdd.split(ds)
-
-# Add small noise to images
 train_ds = cdd.ApplyModifier(
     train_ds,
     lambda pair: pair + np.random.random() * 0.1 - 0.05
 )
 
-# Let's see the metadata we got
-from pprint import pprint
-
 pprint(train_ds.get_meta())
 ```
 
@@ -103,72 +94,36 @@ See all use-cases in [documentation](https://oxid15.github.io/cascade/quickstart
 Not only data and pipelines changes over time. Models change more frequently and require special system to handle experiments and artifacts.
 
 ```python
+import random
 from cascade import models as cdm
 from cascade import data as cdd
 
-from sklearn.datasets import load_breast_cancer
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score, f1_score
-
-
-X, y = load_breast_cancer(return_X_y=True)
-X_train, X_test, y_train, y_test = train_test_split(X, y)
-
-# Define the simple model that using
-# basic methods from cdm.BasicModel
-class BaselineModel(cdm.BasicModel):
-    def __init__(self, const=0, *args, **kwargs) -> None:
-        self.const = const
-        super().__init__(const=const, *args, **kwargs)
-
-    def predict(self, x, *args, **kwargs):
-        return [self.const for _ in range(len(x))]
-
-    # Models define the way whey are trained loaded and saved
-    # we don't use these here, but they exist
-    def fit(self, *args, **kwargs):
-        pass
-
-    def save(self, path):
-        pass
+model = cdm.Model()
+model.metrics.update({
+    'acc': random.random()
+})
 
-
-model = BaselineModel(1)
-
-# Fit and evaluate do not return anything
-model.fit(X_train, y_train)
-model.evaluate(X_test, y_test, {'acc': accuracy_score, 'f1': f1_score})
-
-# Model repository is the solution for experiment and artifact storage
+# Repo is the collection of model lines
 repo = cdm.ModelRepo('repos/use_case_repo')
 
-# Repo is the collection of model lines
 # Line can be a bunch of experiments on one model type
 line = repo.add_line('baseline')
-
-# We save the model - everything is held automatically
 line.save(model, only_meta=True)
-
-from pprint import pprint
-pprint(model.get_meta())
 ```
 
 Let's see what is saved as meta data of this experiment.
 
 ```json
 [
     {
-        "name": "<__main__.BaselineModel object at 0x000001F69F493820>",
-        "created_at": "2023-01-02T16:36:59.041979+00:00",
+        "name": "cascade.models.model.Model",
+        "created_at": "2023-05-29T21:06:23.341752+00:00",
         "metrics": {
-            "acc": 0.6293706293706294,
-            "f1": 0.7725321888412017
-        },
-        "params": {
-            "const": 1
+            "acc": 0.6745652975946803
         },
+        "params": {},
         "type": "model",
-        "saved_at": "2023-01-02T16:36:59.103781+00:00"
+        "saved_at": "2023-05-29T21:06:25.977728+00:00"
     }
 ]
 ```
@@ -189,24 +144,20 @@ from sklearn.datasets import load_digits
 import numpy as np
 
 
-# Load data
 X, y = load_digits(return_X_y=True)
 pairs = [(x, y) for (x, y) in zip(X, y)]
 
-# Let's define a pipeline
 ds = cdd.Wrapper(pairs)
 ds = cdd.RandomSampler(ds)
 train_ds, test_ds = cdd.split(ds)
 
-# Validate using this tool
 cme.PredicateValidator(
     train_ds,
     [
         lambda pair: all(pair[0] < 20),
         lambda pair: pair[1] in (i for i in range(10))
     ]
 )
-
 ```
 
 See all use-cases in [documentation](https://oxid15.github.io/cascade/quickstart.html)
@@ -221,7 +172,6 @@ metrics of all models in repository.
 from cascade import meta as cme
 from cascade import models as cdm
 
-# Open the existing repo
 repo = cdm.ModelRepo('repos/use_case_repo')
 
 # This runs web-server that relies on optional dependency
@@ -244,7 +194,6 @@ cme.HistoryViewer(repo).plot()
 
 # This runs a server ans allows to see changes in real time (for example while models are trained)
 cme.HistoryViewer(repo).serve()
-
 ```
 
 See all use-cases in [documentation](https://oxid15.github.io/cascade/quickstart.html)