You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Use dtreeviz to visualize decision tree

I love the decision tree visualisations available from Dtreeviz library - GitHub , and can duplicate this using 
# Install libraries
!pip install dtreeviz
!apt-get install graphviz

# Sample code
from sklearn.datasets import *
from sklearn import tree
from dtreeviz.trees import *
from IPython.core.display import display, HTML

classifier = tree.DecisionTreeClassifier(max_depth=4)
cancer = load_breast_cancer()

classifier.fit(cancer.data, cancer.target)
viz = dtreeviz(classifier,
               cancer.data,
               cancer.target,
               target_name='cancer',
               feature_names=cancer.feature_names, 
               class_names=["malignant", "benign"],
               fancy=False) 

display(HTML(viz.svg()))

However, when I apply the above to a dtree I made myself, the code bombs out because my data is in a pandas DF (or a np array), not a scikit-learn bunch object. 
Now, over at Sci-kit learn - How to create a Bunch object they tell me pretty sternly not to try to create a bunch object; but I also do not have the skills to convert my DF or NP array to something that the viz function, above, will accept.
We can suppose my DF has nine features and a target, called 'Feature01', 'Feature02', etc and 'Target01'.
This I would normally split thusly
FeatDF  = FullDF.drop( columns = ["Target01"])
LabelDF = FullDF["Target01"]

and then set on my merry way to assign a classifier, or if for ML, create a test/train split.
None of this is helpful when calling dtreeviz - which is expecting things like  "feature_names" (which I take is something included in the "bunch" object). And since I can't convert my DF to a bunch, I'm very much stuck. Oh bring your wisdom, please.
Update: I guess any simple DF would illustrate my conundrum. We could just swing with 
import pandas as pd

Things = {'Feature01': [3,4,5,0], 
          'Feature02': [4,5,6,0], 
          'Feature03': [1,2,3,8], 
          'Target01': ['Red','Blue','Teal','Red']}
DF = pd.DataFrame(Things,
                  columns= ['Feature01', 'Feature02', 
                            'Feature02', 'Target01']) 

as an example DF. Now, would I then go
DataNP = DF.to_numpy()
classifier.fit(DF.data, DF.target)
feature_names = ['Feature01', 'Feature02', 'Feature03'] 
#..and what if I have 50 features...

viz = dtreeviz(classifier,
               DF.data,
               DF.target,
               target_name='Target01',
               feature_names=feature_names, 
               class_names=["Red", "Blue", "Teal"],
               fancy=False) 

or is this daft? Thanks for the guidance so far!

A:

sklearn's decision tree needs numerical target values
You can use sklearn's LabelEncoder to transform your strings to integers
from sklearn import preprocessing

label_encoder = preprocessing.LabelEncoder()
label_encoder.fit(df.Target01)

df['target'] = label_encoder.transform(df.Target01)

dtreeviz expects the class_names to be a list or dict, so let's get it from our label_encoder
class_names = list(label_encoder.classes_)        

Complete code
import pandas as pd
from sklearn import preprocessing, tree
from dtreeviz.trees import dtreeviz

Things = {'Feature01': [3,4,5,0], 
          'Feature02': [4,5,6,0], 
          'Feature03': [1,2,3,8], 
          'Target01': ['Red','Blue','Teal','Red']}
df = pd.DataFrame(Things,
                  columns= ['Feature01', 'Feature02', 
                            'Feature02', 'Target01']) 

label_encoder = preprocessing.LabelEncoder()
label_encoder.fit(df.Target01)
df['target'] = label_encoder.transform(df.Target01)

classifier = tree.DecisionTreeClassifier()
classifier.fit(df.iloc[:,:3], df.target)

dtreeviz(classifier,
         df.iloc[:,:3],
         df.target,
         target_name='toy',
         feature_names=df.columns[0:3],
         class_names=list(label_encoder.classes_)
         )

Old answer
Let's use the cancer dataset to create a Pandas dataframe
df = pd.DataFrame(cancer.data, columns=cancer.feature_names)
df['target'] = cancer.target

which gives us the following dataframe.
mean radius mean texture    mean perimeter  mean area   mean smoothness mean compactness    mean concavity  mean concave points mean symmetry   mean fractal dimension  radius error    texture error   perimeter error area error  smoothness error    compactness error   concavity error concave points error    symmetry error  fractal dimension error worst radius    worst texture   worst perimeter worst area  worst smoothness    worst compactness   worst concavity worst concave points    worst symmetry  worst fractal dimension target
0   17.99   10.38   122.8   1001.0  0.1184  0.2776  0.3001  0.1471  0.2419  0.07871 1.095   0.9053  8.589   153.4   0.006399    0.04904 0.05373 0.01587 0.03003 0.006193    25.38   17.33   184.6   2019.0  0.1622  0.6656  0.7119  0.2654  0.4601  0.1189  0
1   20.57   17.77   132.9   1326.0  0.08474 0.07864 0.0869  0.07017 0.1812  0.05667 0.5435  0.7339  3.398   74.08   0.005225    0.01308 0.0186  0.0134  0.01389 0.003532    24.99   23.41   158.8   1956.0  0.1238  0.1866  0.2416  0.186   0.275   0.08902 0
2   19.69   21.25   130.0   1203.0  0.1096  0.1599  0.1974  0.1279  0.2069  0.05999 0.7456  0.7869  4.585   94.03   0.00615 0.04006 0.03832 0.02058 0.0225  0.004571    23.57   25.53   152.5   1709.0  0.1444  0.4245  0.4504  0.243   0.3613  0.08758 0
[...]
568 7.76    24.54   47.92   181.0   0.05263 0.04362 0.0 0.0 0.1587  0.05884 0.3857  1.428   2.548   19.15   0.007189    0.00466 0.0 0.0 0.02676 0.002783    9.456   30.37   59.16   268.6   0.08996 0.06444 0.0 0.0 0.2871  0.07039 1

and for your classifier it can be used in the following way.
classifier.fit(df.iloc[:,:-1], df.target)

i.e. just take all but the last column as training/input and the target column as the output/target.
The same for the visualization:
viz = dtreeviz(classifier,
               df.iloc[:,:-1],
               df.target,
               target_name='cancer',
               feature_names=df.columns[0:-1],
               class_names=["malignant", "benign"])